Question: A semi-automated modular annotation tool for annotating ancient manuscripts.

Introducing ManuscriptAnnotate: A Web-Based Solution for Ancient Manuscript Annotation
This paper introduces ManuscriptAnnotate, a pioneering web-based platform designed to facilitate the annotation of ancient manuscripts. As a user-friendly tool, it bridges the gap between historians and computer scientists, enabling collaborative annotation of historical documents. ManuscriptAnnotate supports real-time cooperation among geographically dispersed teams, irrespective of the local operating system, making it an accessible solution for scholars worldwide. Moreover, the system is equipped with innovative semi-automatized features that have been found to be particularly effective in annotating manuscripts that have deteriorated over time, streamlining the annotation process for these invaluable historical records.

Industries can improve their business efficiency by analyzing and extracting relevant knowledge from large numbers of documents. Knowledge extraction manually from large volume of documents is labor intensive, unscalable and challenging. Consequently there have been a number of attempts to develop intelligent systems to automatically extract relevant knowledge from OCR documents. Moreover, the automatic system can improve the capability of search engine by providing application-specific domain knowledge. However, extracting the efficient information from OCR documents is challenging due to highly unstructured format [1, 11, 18, 26]. In this paper, we propose an efficient framework for a knowledge extraction system that takes keywords based queries and automatically extracts their most relevant knowledge from OCR documents by using text mining techniques. The framework can provide relevance ranking of knowledge to a given query. We tested the proposed framework on corpus of documents at GE Power where document consists of more than hundred pages in PDF.

We propose a method to create process flow graphs automatically from textbooks for cooking programs. This is realized by understanding context by narrowing down the domain to cooking, and making use of domain specific constraints and knowledge. Since it is relatively easy to extract significant keywords from cooking procedures, we create a domain specific dictionary by statistical methods, and propose a structural analysis method using the dictionary. In order to evaluate the ability of the proposed method, we applied the method to actual procedures as an experiment, which showed effective results. The same experiment was also performed on a different program, which showed lower accuracy but also showed realistic results.

We report on a four year academic research project to build a natural language processing platform in support of a large media company. The Computable News platform processes news stories, producing a layer of structured data that can be used to build rich applications. We describe the underlying platform and the research tasks that we explored building it. The platform supports a wide range of prototype applications designed to support different newsroom functions. We hope that this qualitative review provides some insight into the challenges involved in this type of project.

In a multi-lingual multi-script country like India, a single text line of a document page may contain words of two or more scripts. For the Optical Character Recognition of such a document page it is necessary to identify different scripts from the document. In this paper, an automatic technique for word -wise identification of English, Devnagari and Urdu scripts from a single document is proposed. Here, at first, the document is segmented into lines and then the lines are segmented into possible words. Using characteristics of different scripts, the identification scheme is developed.

We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer.

This paper describes embedding a mathematical formula recognition module into the OCR system OCRopus aiming at developing a OCR system for scientific and technical documents which include mathematical formulas. OCRopus is a open source OCR system emphasizing modularity, easy extensibility, and reuse. This system has several basic components such as preprocessing, layout analysis, and text line recognition, so it is a challenging project to embed the mathematical formula recognition module into the OCRopus system. We have developed the math OCR module, then report how to embed our module into the OCRopus system in order to realize a math OCR which can deal with wide variety of documents including mathematical formulas.

Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE® sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition. To ensure heterogeneity of the corpus, MEDLINE sentences were first scored for term similarity to documents with known gene names, and 10K high- and 10K low-scoring sentences were chosen at random. The original 20K sentences were run through a gene/protein name tagger, and the results were modified manually to reflect a wide definition of gene/protein names subject to a specificity constraint, a rule that required the tagged entities to refer to specific entities. Each sentence in GENETAG was annotated with acceptable alternatives to the gene/protein names it contained, allowing for partial matching with semantic constraints. Semantic constraints are rules requiring the tagged entity to contain its true meaning in the sentence context. Application of these constraints results in a more meaningful measure of the performance of an NER system than unrestricted partial matching. The annotation of GENETAG required intricate manual judgments by annotators which hindered tagging consistency. The data were pre-segmented into words, to provide indices supporting comparison of system responses to the "gold standard". However, character-based indices would have been more robust than word-based indices. GENETAG Train, Test and Round1 data and ancillary programs are freely available at ftp://ftp.ncbi.nlm.nih.gov/pub/tanabe/GENETAG.tar.gz . A newer version of GENETAG-05, will be released later this year.

We present an approach to using a morphological analyzer for tokenizing and morphologically tagging (including partof-speech tagging) Arabic words in one process. We learn classifiers for individual morphological features, as well as ways of using these classifiers to choose among entries from the output of the analyzer. We obtain accuracy rates on all tasks in the high nineties.

We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.

In this paper we present the evaluation results for the creation of WordNets for five languages (Spanish, French, German, Italian and Portuguese) using an approach based on parallel corpora. We have used three very large parallel corpora for our experiments: DGT-TM, EMEA and ECB. The English part of each corpus is semantically tagged using Freeling and UKB. After this step, the process of WordNet creation is converted into a word alignment problem, where we want to align WordNet synsets in the English part of the corpus with lemmata on the target language part of the corpus. The word alignment algorithm used in these experiments is a simple most frequent translation algorithm implemented into the WN-Toolkit. The obtained precision values are quite satisfactory, but the overall number of extracted synset-variant pairs is too low, leading into very poor recall values. In the conclusions, the use of more advanced word alignment algorithms, such as Giza++, Fast Align or Berkeley aligner is suggested.


Question: A semi-automated modular annotation tool for the annotation of ancient manuscripts.

This paper presents OCRopus, an innovative open-source Optical Character Recognition (OCR) system that prioritizes modularity, flexibility, and reusability, making it suitable for both academic research and extensive commercial document processing. The current state of the system, its overarching design, and the principal algorithms employed for analyzing document layouts and recognizing text lines are thoroughly detailed herein. OCRopus is designed to be adaptable and scalable, catering to a wide range of document conversion requirements. Its modular architecture facilitates easy extension and customization, allowing researchers and developers to integrate specific components or algorithms to suit their particular needs. This makes OCRopus a versatile tool for handling large-scale document conversion projects, while also serving as a robust platform for advancing OCR technology through research and development.

Efficient management of text data is a major concern of business organizations. In this direction, we propose a novel approach to extract structured knowledge from large corpora of unstructured business documents. This knowledge is represented in the form of object instances, which are common ways of organizing the available information about entities, and are modeled here using document templates. The approach itself is based on the observation that a significant fraction of these documents are created using the cut-copy-pastemethod, and thus, it is important to factor this observation into business document analysis projects. Correspondingly, our approach solves the problem of object instance extraction in two steps, namely similarity search and then extraction of object instances from the selected documents. Early qualitative results on a couple of carefully selected document corpora indicate the effective applicability of the approach for solving an important component of the efficient text management problem.

in large multimedia databases and make inferences about the data stream’s content. A human operator can specify gross indices, such as the date or hour, and perhaps the location and the activity (for example, a football game) at the time of capture. Finer-grain annotations require separating the video into meaningful segments such as shots and scenes. Many automated techniques and annotation formats for capturing this type of information exist. However, the descriptions they produce don’t define the data stream content adequately for semantic retrieval and reasoning. Such descriptions must be based on observable or inferrable events in the data streams. We describe a framework for video event representation and annotation that’s based on the definition of an ontology suitable for video content. An ontology consists of a specific vocabulary for describing a certain reality and a set of explicit assumptions regarding the vocabulary’s intended meaning.1 The Video Event Representation Language (VERL)2-4 describes an event ontology, and the Video Event Markup Language (VEML)4 lets us annotate instances of the events described in VERL (see the sidebar “Using VERL and VEML” on p. 78 for an example). Annotating video in VEML consists of describing instances of events and objects, in a previously defined ontology using VERL. Figure 1 illustrates the conceptual elements involved and their relationships: annotations draw on one or several domain-specific ontologies, defined according to VERL concepts and constructs. VEML incorporates extensible event and object type hierarchies rooted in VERL’s event and object concepts. The annotations also draw on VEML for content organization. Marking up data streams in a well-defined language rooted in an ontology enables nonambiguous data sharing among users. Furthermore, annotation data is accessible to automatic machine manipulation for indexing or inferencing. The framework we describe resulted from discussions at a series of workshops sponsored by the US government’s Advanced Research and Development Activity (ARDA). It therefore includes the intellectual contributions of many individuals. Currently, only a small community of researchers is using VERL and VEML; this article aims to publicize the framework to a broader community as its relevance depends on its widespread adoption. An earlier description can be found elsewhere.5

In this paper, we focus on the development of a framework for automatic metadata generation. The first step towards this framework is the definition of an Application Programmer Interface (API), which we call the Simple Indexing Interface (SII). The second step is the definition of a framework for implementation of the SII. Both steps are presented in some detail in this paper. We also report on empirical evaluation of the metadata that the SII and supporting framework generated in a real-life context.

With the continued demand for Islamic knowledge, which is mainly based on the Quran as a source of knowledge and wisdom, systems that facilitate an easy search of the content of the Quran remain a considerable challenge. Although in recent years there have been tools for Quran search, most of these tools are based on keyword search, meaning that the user needs to know the correct keywords before being able to retrieve the content of al-Quran. In this paper, we propose a system that supports the end user in querying and exploring the Quran ontology. The system comprises user query reformulation against the Quran ontology stored and annotated in the knowledge base. The Quran ontology consists of noun concepts identified in al-Quran, and the relationship that exists between these concepts. The user writes a query in the natural language and the proposed system reformulates the query to match the content found in the knowledge base in order to retrieve the relevant answer. The answer is represented by the Quranic verse related to the user query.

Figures are very important non-textual information contained in scientific documents. Current digital libraries do not provide users tools to retrieve documents based on the information available within the figures. We propose an architecture for retrieving documents by integrating figures and other information. The initial step in enabling integrated document search is to categorize figures into a set of pre-defined types. We propose several categories of figures based on their functionalities in scholarly articles. We have developed a machine-learning-based approach for automatic categorization of figures. Both global features, such as texture, and part features, such as lines, are utilized in the architecture for discriminating among figure categories. The proposed approach has been evaluated on a testbed document set collected from the CiteSeer scientific literature digital library. Experimental evaluation has demonstrated that our algorithms can produce acceptable results for realworld use. Our tools will be integrated into a scientific document digital library.

The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering of ontologies and avoidance of a knowledge acquisition bottleneck. Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer. The vision of ontology learning that we propose here includes a number of complementary disciplines that feed on di erent types of unstructured, semi-structured and fully structured data in order to support a semi-automatic, cooperative ontology engineering process. Our ontology learning framework proceeds through ontology import, extraction, pruning, re nement, and evaluation giving the ontology engineer a wealth of coordinated tools for ontology modeling. Besides of the general framework and architecture, we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, TextTo-Onto, such as ontology learning from free text, from dictionaries, or from legacy ontologies, and refer to some others that need to complement the complete architecture, such as reverse engineering of ontologies from database schemata or learning from XML documents. 1. ONTOLOGIES FOR THE SEMANTIC WEB Conceptual structures that de ne an underlying ontology are germane to the idea of machine processable data on the Semantic Web. Ontologies are (meta)data schemas, providing a controlled vocabulary of concepts, each with an explicitly de ned and machine processable semantics. By de ning shared and common domain theories, ontologies help both people and machines to communicate concisely, supporting the exchange of semantics and not only syntax. Hence, the Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission by the authors. Semantic Web Workshop 2001 Hongkong, China Copyright by the authors. cheap and fast construction of domain-speci c ontologies is crucial for the success and the proliferation of the Semantic Web. Though ontology engineering tools have become mature over the last decade (cf. [9]), the manual acquisition of ontologies still remains a tedious, cumbersome task resulting easily in a knowledge acquisition bottleneck. Having developed our ontology engineering workbench, OntoEdit, we had to face exactly this issue, in particular we were given questions like Can you develop an ontology fast? (time) Is it diÆcult to build an ontology? (diÆculty) How do you know that you've got the ontology right? (con dence) In fact, these problems on time, diÆculty and con dence that we ended up with were similar to what knowledge engineers had dealt with over the last two decades when they elaborated on methodologies for knowledge acquisition or workbenches for de ning knowledge bases. A method that proved extremely bene cial for the knowledge acquisition task was the integration of knowledge acquisition with machine learning techniques [33]. The drawback of these approaches, e.g. the work described in [21], however, was their rather strong focus on structured knowledge or data bases, from which they induced their rules. In contrast, in the Web environment that we encounter when building Web ontologies, the structured knowledge or data base is rather the exception than the norm. Hence, intelligent means for an ontology engineer takes on a di erent meaning than the | very seminal | integration architectures for more conventional knowledge acquisition [7]. Our notion of Ontology Learning aims at the integration of a multitude of disciplines in order to facilitate the construction of ontologies, in particular machine learning. Because the fully automatic acquisition of knowledge by machines remains in the distant future, we consider the process of ontology learning as semi-automatic with human intervention, adopting the paradigm of balanced cooperative modeling [20] for the construction of ontologies for the Semantic Web. This objective in mind, we have built an architecture that combines knowledge acquisition with machine learning, feeding on the resources that we nowadays nd on the syntactic Web, viz. free text, semi-structured text, schema de nitions (DTDs), etc. Thereby, modules in our framework serve different steps in the engineering cycle, which here consists of the following ve steps (cf. Figure 1): First, existing ontologies are imported and reused by merging existing structures or de ning mapping rules between existing structures and the ontology to be established. For instance, [26] describe how ontological structures contained in Cyc are used in order to facilitate the construction of a domain-speci c ontology. Second, in the ontology extraction phase major parts of the target ontology are modeled with learning support feeding from web documents. Third, this rough outline of the target ontology needs to be pruned in order to better adjust the ontology to its prime purpose. Fourth, ontology re nement pro ts from the given domain ontology, but completes the ontology at a ne granularity (also in contrast to extraction). Fifth, the prime target application serves as a measure for validating the resulting ontology [31]. Finally, one may revolve again in this cycle, e.g. for including new domains into the constructed ontology or for maintaining and updating its scope. 2. THE ONTOLOGY LEARNING KILLER APPLICATION Though ontologies and their underlying data in the Semantic Web are envisioned to be reusable for a wide range of possibly unforeseen applications, a particular target application remains the touchstone for a given ontology. In our case, we have been dealing with ontology-based knowledge portals that structure Web content and that allow for structured provisioning and accessing of data [29, 30]. Knowledge portals are information intermediaries for knowledge accessing and sharing on the Web. The development of a knowledge portal consists of the tasks of structuring the knowledge, establishing means for providing new knowledge and accessing the knowledge contained in the portal. A considerable part of development and maintenance of the portal lies in integrating legacy information as well as in constructing and maintaining the ontology in vast, often unknown, terrain. For instance, a knowledge portal may focus on the electronics sector, integrating comparative shopping in conjunction with manuals, reports and opinions about current electronic products. The creation of the background ontology for this knowledge portal involves tremendous e orts for engineering the conceptual structures that underly existing warehouse databases, product catalogues, user manuals, test reports and newsgroup discussions. Correspondingly, ontology structures must be constructed from database schemata, a given product thesaurus (like BMEcat), XML documents and document type de nitions (DTDs), and free texts. Still worse, signi cant parts of these (meta-)data change extremely fast and, hence, require a regular update of the corresponding ontology parts. Thus, very di erent types of (meta-)data might be useful input for the construction of the ontology. However, in practice one needs comprehensive support in order to deal Just like Tim Berners-Lee did not forsee online auctions being a common business model of the Web of 2000. Comprehensive support with ontology learning need not necessarily imply the top-notch learning algorithms, but may rely more heavily on appropriate tool support and methodology. with this wealth. Hence, there comes the need for a range of di erent techniques: Structured data and meta data require reverse engineering approaches, free text may contribute to ontology learning directly or through information extraction approaches. Semi-structured data may nally require and pro t from both. In the following we elaborate on our ontology learning framework. Thereby we approach di erent techniques for di erent types of data, showing parts of our architecture, its current status, and parts that may complement our current Text-To-Onto environment. A general overview of ontology learning techniques as well as corresponding references may be found in Section 9. 3. AN ARCHITECTURE FOR ONTOLOGY LEARNING Given the task of constructing and maintaining an ontology for a Semantic Web application, e.g. for an ontologybased knowledge portal that we have been dealing with (cf. [29]), we have produced a wish list of what kind of support we would fancy. 3.1 Ontology Engineering WorkbenchOntoEdit As core to our approach we have built a graphical user interface to support the ontology engineering process manually performed by the ontology engineer. Here, we o er sophisticated graphical means for manual modeling and re ning the nal ontology. Di erent views are o ered to the user targeting the epistemological level rather than a particular representation language. However, the ontological structures built there may be exported to standard Semantic Web representation languages, such as OIL and DAML-ONT, as well as our own F-Logic based extensions of RDF(S). In addition, executable representations for constraint checking and application debugging can be generated and then accessed via SilRi, our F-Logic inference engine, that is directly connected with OntoEdit. The sophisticated ontology engineering tools we knew, e.g. the Prot eg e modeling environment for knowledge-based systems [9], would o er capabilities roughly comparable to OntoEdit. However, given the task of constructing a knowledge portal, we f

We investigate the identification and, to some extent, the classification of collocational word groups that consist of an adjectival modifier (A), an accusative object noun (N), and a verb (V) by means of parsing a newspaper corpus with a lexicalized probabilistic grammar.1 Data triples are extracted from the resulting Viterbi parses and subsequently evaluated by means of a statistical association measure. The extraction results are then compared to predefined descriptive classes of ANV-triples. We also use a decision tree algorithm to classify part of the data obtained, on the basis of a small set of manually classified examples. Much of the candidate data is lexicographically relevant: the triples include idiomatic combinations (e.g. (sich) eine goldene Nase verdienen, ‘get rich’, lit. ‘earn oneself a golden nose’), combinations of N+V and N+A collocations (e.g. (eine) klare Absage erteilen, ‘refuse resolutely’, lit. ‘give a clear refusal’), next to cases where N+V or N+A collocations are found, in combination with other (not necessarily collocational) context partners. To extract such data from text corpora, a grammar is needed that captures verb+object relations: simple pattern matching on part-of-speech shapes is not sufficient. Statistical tools then allow to order the data in a way useful for subsequent manual selection by lexicographers. 1This work has been carried out in the context of the Transferbereich 32: Automatische Exzerption, a DFG-funded project aiming at the creation of support tools for the corpus-based updating of printed dictionaries in lexicography, carried out in cooperation with the publishers Langenscheidt KG and Duden BIFAB AG.

Geographic information extraction from textual data sources, called geoparsing, is a key task in text processing and central to subsequent spatial analysis approaches. Several geoparsers are available that support this task, each with its own (often limited or specialized) gazetteer and its own approaches to toponym detection and resolution. In this demonstration paper, we present HeidelPlace, an extensible framework in support of geoparsing. Key features of HeidelPlace include a generic gazetteer model that supports the integration of place information from different knowledge bases, and a pipeline approach that enables an effective combination of diverse modules tailored to specific geoparsing tasks. This makes HeidelPlace a valuable tool for testing and evaluating different gazetteer sources and geoparsing methods. In the demonstration, we show how to set up a geoparsing workflow with HeidelPlace and how it can be used to compare and consolidate the output of different geoparsing approaches.

End-to-end Optical Character Recognition (OCR) systems are heavily used to convert document images into machine-readable text. Commercial and open-source OCR systems (like Abbyy, OCRopus, Tesseract etc.) have traditionally been optimized for contemporary documents like books, letters, memos, and other end-user documents. However, these systems are difficult to use equally well for digitizing historical document images, which contain degradations like non-uniform shading, bleed-through, and irregular layout; such degradations usually do not exist in contemporary document images.
 The open-source anyOCR is an end-to-end OCR pipeline, which contains state-of-the-art techniques that are required for digitizing degraded historical archives with high accuracy. However, high accuracy comes at a cost of high computational complexity that results in 1) long runtime that limits digitization of big collection of historical archives and 2) high energy consumption that is the most critical limiting factor for portable devices with constrained energy budget. Therefore, we are targeting energy efficient and high throughput acceleration of the anyOCR pipeline. Generalpurpose computing platforms fail to meet these requirements that makes custom hardware design mandatory. In this paper, we are presenting a new concept named iDocChip. It is a portable hybrid hardware-software FPGA-based accelerator that is characterized by low footprint meaning small size, high power efficiency that will allow using it in portable devices, and high throughput that will make it possible to process big collection of historical archives in real time without effecting the accuracy.
 In this paper, we focus on binarization, which is the second most critical step in the anyOCR pipeline after text-line recognizer that we have already presented in our previous publication [21]. The anyOCR system makes use of a Percentile Based Binarization method that is suitable for overcoming degradations like non-uniform shading and bleed-through. To the best of our knowledge, we propose the first hardware architecture of the PBB technique. Based on the new architecture, we present a hybrid hardware-software FPGA-based accelerator that outperforms the existing anyOCR software implementation running on i7-4790T in terms of runtime by factor of 21, while achieving energy efficiency of 10 Images/J that is higher than that achieved by low power embedded processors with negligible loss of recognition accuracy.

We report on a four year academic research project to build a natural language processing platform in support of a large media company. The Computable News platform processes news stories, producing a layer of structured data that can be used to build rich applications. We describe the underlying platform and the research tasks that we explored building it. The platform supports a wide range of prototype applications designed to support different newsroom functions. We hope that this qualitative review provides some insight into the challenges involved in this type of project.


Question: A semi-automated modular tool for annotating ancient manuscripts.

This paper introduces an innovative semi-automatic framework designed to streamline the creation of ground truth for historical manuscripts, which is essential for the training and assessment of document analysis techniques. The process of manually generating ground truth can be painstakingly laborious, particularly when dealing with intricate historical manuscripts characterized by convoluted layouts and overlapping handwriting. Our proposed system aims to alleviate this issue by leveraging document graphs and pen-based scribbling interaction. Document graphs offer a concise representation of the page, closely resembling the desired ground truth, while scribbling interaction ensures an efficient and user-friendly pen-based engagement with the graph. The effectiveness of our system is showcased through its application to a recently developed database of historical manuscripts with complex layouts, demonstrating its potential to significantly expedite the annotation process without compromising on accuracy.

We propose a method for automatically labelling topics learned via LDA topic models. We generate our label candidate set from the top-ranking topic terms, titles of Wikipedia articles containing the top-ranking topic terms, and sub-phrases extracted from the Wikipedia article titles. We rank the label candidates using a combination of association measures and lexical features, optionally fed into a supervised ranking model. Our method is shown to perform strongly over four independent sets of topics, significantly better than a benchmark method.

There is a large collection of Handwritten English paper documents of Historical and Scientific importance. But paper documents are not recognised directly by computer. Hence the closest way of indexing these documents is by storing their document digital image. Hence a large database of document images can replace the paper documents. But the document and data corresponding to each image cannot be directly recognised by the computer. This paper applies the technique of word spotting using Modified Character Shape Code to Handwritten English document images for quick and efficient query search of words on a database of document images. It is different from other Word Spotting techniques as it implements two level of selection for word segments to match search query. First based on word size and then based on character shape code of query. It makes the process faster and more efficient and reduces the need of multiple pre-processing.

Characterizing the content of a technical document in terms of its learning utility can be useful for applications related to education, such as generating reading lists from large collections of documents. We refer to this learning utility as the “pedagogical value” of the document to the learner.While pedagogical value is an important concept that has been studied extensively within the education domain, there has been little work exploring it from a computational, i.e., natural language processing (NLP), perspective. To allow a computational exploration of this concept, we introduce the notion of “pedagogical roles” of documents (e.g., Tutorial and Survey) as an intermediary component for the study of pedagogical value. Given the lack of available corpora for our exploration, we create the first annotated corpus of pedagogical roles and use it to test baseline techniques for automatic prediction of such roles.

Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from them (when using knowledge engineering). In this work, we describe away to reduce this effort, while retaining the methods' accuracy, by constructing a hybrid classifier that utilizes human reasoning over automatically discovered text patterns to complement machine learning. Using a standard sentiment-classification dataset and real customer feedback data, we demonstrate that the resulting technique results in significant reduction of the human effort required to obtain a given classification accuracy. Moreover, the hybrid text classifier also results in a significant boost in accuracy over machine-learning based classifiers when a comparable amount of labeled data is used.

We propose a semi-supervised model which segments and annotates images using very few labeled images and a large unaligned text corpus to relate image regions to text labels. Given photos of a sports event, all that is necessary to provide a pixel-level labeling of objects and background is a set of newspaper articles about this sport and one to five labeled images. Our model is motivated by the observation that words in text corpora share certain context and feature similarities with visual objects. We describe images using visual words, a new region-based representation. The proposed model is based on kernelized canonical correlation analysis which finds a mapping between visual and textual words by projecting them into a latent meaning space. Kernels are derived from context and adjective features inside the respective visual and textual domains. We apply our method to a challenging dataset and rely on articles of the New York Times for textual features. Our model outperforms the state-of-the-art in annotation. In segmentation it compares favorably with other methods that use significantly more labeled training data.

Summary form only given. The paper describes a novel automatic procedure for the generation of multiple-choice tests from electronic documents. In addition to employing various NLP techniques including term extraction and shallow parsing, the system makes use of language resources such as corpora and ontologies. The system operates in a fully automatic mode and also a semiautomatic environment where the user is offered the option to post-edit the generated test items. The results from the conducted evaluation suggest that the new procedure is very effective saving time and labour considerably and that the test items produced with the help of the program are not of inferior quality to those produced manually.

This article describes the challenges posed by optical music recognition – a topic in computer science that aims to convert scanned pages of music into an on-line format. First, the problem is described; then a generalised framework for software is presented that emphasises key stages that must be solved: staff line identification, musical object location, musical feature classification, and musical semantics. Next, significant research projects in the area are reviewed, showing how each fits the generalised framework. The article concludes by discussing perhaps the most open question in the field: how to compare the accuracy and success of rival systems, highlighting certain steps that help ease the task.

Automatic image annotation is an effective way for managing and retrieving abundant images on the internet. In this paper, a bipartite graph reinforcement model (BGRM) is proposed for web image annotation. Given a web image, a set of candidate annotations is extracted from its surrounding text and other textual information in the hosting web page. As this set is often incomplete, it is extended to include more potentially relevant annotations by searching and mining a large-scale image database. All candidates are modeled as a bipartite graph. Then a reinforcement algorithm is performed on the bipartite graph to re-rank the candidates. Only those with the highest ranking scores are reserved as the final annotations. Experimental results on real web images demonstrate the effectiveness of the proposed model.

In this paper we present a new database for off-line handwriting recognition, together with a few preprocessing and text segmentation procedures. The database is based on the Lancaster-Oslo/Bergen(LOB) corpus. This corpus is a collection of texts that were used to generate forms, which subsequently were filled out by persons with their handwriting. Up to now (December 1998) the database includes 556 forms produced by approximately 250 different writers. The database consists of full English sentences. It can serve as a basis for a variety of handwriting recognition tasks. The main focus, however, is on recognition techniques that use linguistic knowledge beyond the lexicon level. This knowledge can be automatically derived from the corpus or it can be supplied from external sources.

We present a new approach to evaluate computational models for the task of text understanding by the means of out-of-context error detection. Through the novel design of our automated modification process, existing large-scale data sources can be adopted for a vast number of text understanding tasks. The data is thereby altered on a semantic level, allowing models to be tested against a challenging set of modified text passages that require to comprise a broader narrative discourse. Our newly introduced task targets actual real-world problems of transcription and translation systems by inserting authentic out-of-context errors. The automated modification process is applied to the 2016 TEDTalk corpus. Entirely automating the process allows the adoption of complete datasets at low cost, facilitating supervised learning procedures and deeper networks to be trained and tested. To evaluate the quality of the modification algorithm a language model and a supervised binary classification model are trained and tested on the altered dataset. A human baseline evaluation is examined to compare the results with human performance. The outcome of the evaluation task indicates the difficulty to detect semantic errors for machine-learning algorithms and humans, showing that the errors cannot be identified when limited to a single sentence.


Question: A semi-automated modular tool for annotating ancient manuscripts.

Introducing Aletheia: A Sophisticated Document Layout and Text Ground-Truthing System for High-Volume Digitisation Environments

The proliferation of large-scale digitisation projects has paved the way for innovative methods in Document Image Analysis and Optical Character Recognition (OCR), particularly those that are adaptive and learning-based. However, a significant challenge remains in the area of ground truth production for extensive document collections, where productivity and efficiency are paramount. Ground truth data is indispensable for the training and evaluation of tools during their development phase, as well as for maintaining quality control in the digitisation workflows of digital libraries. This paper presents Aletheia, a cutting-edge system designed to facilitate the cost-effective and precise creation of ground truth for voluminous document sets.

Aletheia is equipped with a suite of automated and semi-automated tools, developed and refined through feedback from major European libraries and their digitisation service providers, who have been using the system in production settings. Among its innovative features are the support for top-down ground truthing with advanced split and shrink functionalities, and bottom-up ground truthing that enables the aggregation of basic elements into more intricate structures. The system is particularly adept at handling the complexities of historical documents, thanks to specialized features developed for this purpose. Additionally, Aletheia incorporates a robust rules and guidelines validator, complemented by powerful correction tools, which together ensure the rapid production of highly accurate ground truth data.

Aletheia represents a significant advancement in the field of document image analysis, offering a comprehensive solution for the efficient and accurate ground truthing of large document collections, particularly in the context of historical manuscripts and documents. Its application in high-volume digitisation environments ensures that the ground truth data produced is of the highest quality, making it invaluable for both tool development and quality assurance in digital library workflows.

Citations management is an important task in managing digital libraries. Citations provide valuable information e.g., used in evaluating an author's influences or scholarly quality (the impact factor of research journals). But although a reliable and effective autonomous citation management is essential, manual citation management can be extremely costly. Automatic citation mining on the other hand is a non-trivial task mainly due to non-conforming citation styles, spelling errors and the difficulty of reliably extracting text from PDF documents. In this paper we propose a novel rule-based autonomous citation mining technique, to address this important task. We define a set of common heuristics that together allow to improve the state of the art in automatic citation mining. Moreover, by first disambiguating citations based on venues, our technique significantly enhances the correct discovery of citations. Our experiments show that the proposed approach is indeed able to overcome limitations of current leading citation indexes such as ISI Web of Knowledge , Citeseer and Google Scholar.

After providing a brief introduction to the transliteration problem, and highlighting some issues specific to Arabic to English translation, a three phase algorithm is introduced as a computational solution to the problem. The algorithm is based on a Hidden Markov Model approach, but also leverages information available in on-line databases. The algorithm is then evaluated, and shown to achieve accuracy approaching 80%.

Text extraction tools are vital for obtaining the textual content of computer files and for using the electronic text in a wide variety of applications, including search and natural language processing. However, when extraction tools fail, they convert once reliable electronic text into garbled text, or no text at all. The techniques and tools for validating the accuracy of these text extraction tools are conspicuously absent from academia and industry. This paper contributes to closing this gap. We discuss an exploratory investigation into a method and a set of tools for evaluating a text extraction toolkit. Although this effort focuses on the popular open source Apache Tika toolkit and the govdocs1 corpus, the method generally applies to other text extraction toolkits and corpora.

Parallel corpus is a rich linguistic resource for various multilingual text management tasks, including crosslingual text retrieval, multilingual computational linguistics and multilingual text mining. Constructing a parallel corpus requires effective alignment of parallel documents. In this paper, we develop a parallel page identification system for identifying and aligning parallel documents from the World Wide Web. The system crawls the Web to fetch potentially parallel multilingual Web documents using a Web spider. To determine the parallelism between potential document pairs, two modules are developed. First, a filename comparison module is used to check filename resemblance. Second, a content analysis module is used to measure the semantic similarity. The experiment conducted to a multilingual Web site shows the effectiveness of the system.

SUMMARIST is an attempt to create a robust automated text summarization system, based on the ‘equation’: summarization = topic identification + interpretation + generation. Each of these stages contains several independent modules, many of them trained on large corpora of text. We describe the system’s architecture and provide details of some of its modules.

In this paper we introduce SAMOD, a.k.a. Simplified Agile Methodology for Ontology Development, a novel agile methodology for the development of ontologies by means of small steps of an iterative workflow that focuses on creating well-developed and documented models starting from exemplar domain descriptions. RASH: https://w3id.org/people/essepuntato/papers/samod-owled2016.html

We propose a method to create process flow graphs automatically from textbooks for cooking programs. This is realized by understanding context by narrowing down the domain to cooking, and making use of domain specific constraints and knowledge. Since it is relatively easy to extract significant keywords from cooking procedures, we create a domain specific dictionary by statistical methods, and propose a structural analysis method using the dictionary. In order to evaluate the ability of the proposed method, we applied the method to actual procedures as an experiment, which showed effective results. The same experiment was also performed on a different program, which showed lower accuracy but also showed realistic results.

We describe the Photobook system, which is a set of interactive tools for browsing and searching images and image sequences. These query tools differ from those used in standard image databases in that they make direct use of the image content rather than relying on text annotations. Direct search on image content is made possible by use of semantics-preserving image compression, which reduces images to a small set of perceptually-significant coefficients. We discuss three types of Photobook descriptions in detail: one that allows search based on appearance, one that uses 2-D shape, and a third that allows search based on textural properties. These image content descriptions can be combined with each other and with text-based descriptions to provide a sophisticated browsing and search capability. In this paper we demonstrate Photobook on databases containing images of people, video keyframes, hand tools, fish, texture swatches, and 3-D medical data.

Stylometry today uses either stand-alone dedicated programs, custom-made by stylometrists, or applies existing software, often one for each stage of the analysis. Stylometry with R can be placed somewhere in-between, as the powerful open-source statistical programming environment provides, on the one hand, the opportunity of building statistical applications from scratch, and, on the other, allows less advanced researchers to use ready-made scripts and libraries. In our own stylometric adventure with R, one of the aims was to build a tool (or a set of tools) that would combine sophisticated state-of-the-art algorithms of classification and/or clustering with a user-friendly interface. In particular, we wanted to implement a number of multidimensional methods that could be used by scholars without programming skills. And more: it soon became evident that once our R scripts are made, provided with a graphic user interface and more or less documented, they are highly usable in class; experience shows that this is an excellent way to work around R’s normally steep learning curve without losing anything of the environment’s considerable computing power and speed.

Ever growing number of image documents available on the Internet continuously motivates research in better annotation models and more efficient retrieval methods. Formal knowledge representation of objects and events in pictures, their interaction as well as context complexity becomes no longer an option for a quality image repository, but a necessity. We present an ontologybased online image annotation tool WNtags and demonstrate its usefulness in several typical multimedia retrieval tasks using International Affective Picture System emotionally annotated image database. WNtags is built around WordNet lexical ontology but considers Suggested Upper Merged Ontology as the preferred labeling formalism. WNtags uses sets of weighted WordNet synsets as high-level image semantic descriptors and query matching is performed with word stemming and node distance metrics. We also elaborate our near future plans to expand image content description with induced affect as in stimuli for research of human emotion and attention.


Question: A semi-automated modular tool for annotating ancient manuscripts.

This paper introduces a web-based annotation tool and database, LabelMe, designed to facilitate the creation of a large, diverse image collection with detailed ground truth labels. This annotated data serves as a valuable resource for advancing object detection and recognition research, particularly in supervised learning and performance evaluation. The LabelMe tool streamlines the image annotation process and enables seamless sharing of annotations, making it a powerful asset for data collection. Through this platform, a substantial dataset encompassing a broad range of object categories has been amassed, often featuring multiple instances of objects across various images. The paper provides a comprehensive analysis of the dataset's composition and benchmarks it against leading datasets used in object recognition and detection. Additionally, the authors demonstrate methods to augment the dataset automatically, such as enriching object labels with WordNet information, identifying object parts, inferring the depth order of objects within scenes, and expanding the number of labels with minimal human oversight and the integration of web-sourced images. This approach not only enhances the dataset's richness but also underscores the potential for semi-automated tools in advancing annotation practices for complex visual data.

**Question**: An Efficient and Robust Method for Real-time Pose Estimation of Multiple Objects

**Documents**: Real-time Object Pose Estimation using a Multi-Stage Deep Neural Network
We present a real-time object pose estimation system that uses a multi-stage deep neural network (DNN). The system first detects objects in an image using a region proposal network, then estimates the pose of each object using a cascade of DNNs. The first stage of the cascade is a coarse pose estimator, which is used to initialize the pose for the subsequent stages. The following stages refine the pose estimate, with each stage focusing on different parts of the object. The system is trained end-to-end using a combination of 3D object models and real images. We evaluate the system on the LINEMOD and Occlusion datasets and compare it to state-of-the-art methods. The results show that our system outperforms existing methods in terms of accuracy and speed, making it suitable for real-time applications.

**Similar Documents**: This paper introduces an innovative real-time object pose estimation system that harnesses the power of a multi-stage deep neural network (DNN) architecture. The proposed system begins by identifying objects within an image using a region proposal network, followed by a cascade of DNNs to estimate the pose of each detected object. The cascade initiates with a coarse pose estimator to provide an initial pose estimate, which is subsequently refined by subsequent stages. Each subsequent stage

Populating large-scale structured databases from unstructured sources is a critical and challenging task in data analytics. As automated feature engineering methods grow increasingly prevalent, constructing sufficiently large labeled training sets has become the primary hurdle in building machine learning information extraction systems. In light of this, we have taken a new approach called data programming [7]. Rather than hand-labeling data, in the data programming paradigm, users generate large amounts of noisy training labels by programmatically encoding domain heuristics as simple rules. Using this approach over more traditional distant supervision methods and fully supervised approaches using labeled data, we have been able to construct knowledge base systems more rapidly and with higher quality. Since the ability to quickly prototype, evaluate, and debug these rules is a key component of this paradigm, we introduce DDLite, an interactive development framework for data programming. This paper reports feedback collected from DDLite users across a diverse set of entity extraction tasks. We share observations from several DDLite hackathons in which 10 biomedical researchers prototyped information extraction pipelines for chemicals, diseases, and anatomical named entities. Initial results were promising, with the disease tagging team obtaining an F1 score within 10 points of the state-of-the-art in only a single day-long hackathon's work. Our key insights concern the challenges of writing diverse rule sets for generating labels, and exploring training data. These findings motivate several areas of active data programming research.

This paper describes the fourth edition of the Handwritten Text Recognition (HTR) competition that was prepared this time in the context of the International Conference on Document Analysis and Recognition (ICDAR) 2017. Previous editions of this competition were conducted, first, with datasets from the tranScriptorium project in ICFHR 2014, and ICDAR 2015, and then, with datasets from the "Recognition and Enrichment of Archival Documents (READ)" European project in ICFHR 2016. This competition aims to bring together researchers working on off-line HTR and provides them a suitable benchmark to compare their techniques on the task of transcribing typical and difficult historical handwritten documents. The competition proposed for ICDAR 2017 aims at introducing a usual scenario for some collections in which there exist transcripts at page level for many pages useful for training, but these transcripts are not aligned with line images. Two tracks with different conditions on the use of training data were proposed. Most of the data comes from the Alfred Escher Letter Collection. But handwritten images were drawn from other German collections written by several hands.

Web tables constitute valuable sources of information for various applications, ranging from Web search to Knowledge Base (KB) augmentation. An underlying common requirement is to annotate the rows of Web tables with semantically rich descriptions of entities published in Web KBs. In this paper, we evaluate three unsupervised annotation methods: (a) a lookup-based method which relies on the minimal entity context provided in Web tables to discover correspondences to the KB, (b) a semantic embeddings method that exploits a vectorial representation of the rich entity context in a KB to identify the most relevant subset of entities in the Web table, and (c) an ontology matching method, which exploits schematic and instance information of entities available both in a KB and a Web table. Our experimental evaluation is conducted using two existing benchmark data sets in addition to a new large-scale benchmark created using Wikipedia tables. Our results show that: 1) our novel lookup-based method outperforms state-of-theart lookup-based methods, 2) the semantic embeddings method outperforms lookup-based methods in one benchmark data set, and 3) the lack of a rich schema in Web tables can limit the ability of ontology matching tools in performing high-quality table annotation. As a result, we propose a hybrid method that significantly outperforms individual methods on all the benchmarks.

We describe efforts to adapt the Tesseract open source OCR engine for multiple scripts and languages. Effort has been concentrated on enabling generic multi-lingual operation such that negligible customization is required for a new language beyond providing a corpus of text. Although change was required to various modules, including physical layout analysis, and linguistic post-processing, no change was required to the character classifier beyond changing a few limits. The Tesseract classifier has adapted easily to Simplified Chinese. Test results on English, a mixture of European languages, and Russian, taken from a random sample of books, show a reasonably consistent word error rate between 3.72% and 5.78%, and Simplified Chinese has a character error rate of only 3.77%.

This paper presents a survey of basic concepts in the area of text data mining and some of the methods used in order to elicit useful knowledge from collections of textual data. Three different text data mining techniques (clustering/visualisation, association rules and classification models) are analysed and its exploitation possibilities within the Webocracy project are showed. Clustering and association rules discovery are well suited as supporting tools for ontology management. Classification models are used for automatic documents annotation.

In this paper, we explore the task of automatic text processing applied to collections of historical newspapers, with the aim of assisting historical research. In particular, in this first stage of our project, we experiment with the use of topical models as a means to identify potential issues of interest for historians. 1 Newspapers in Historical Research Surviving newspapers are among the richest sources of information available to scholars studying peoples and cultures of the past 250 years, particularly for research on the history of the United States. Throughout the nineteenth and twentieth centuries, newspapers served as the central venues for nearly all substantive discussions and debates in American society. By the mid-nineteenth century, nearly every community (no matter how small) boasted at least one newspaper. Within these pages, Americans argued with one another over politics, advertised and conducted economic business, and published articles and commentary on virtually all aspects of society and daily life. Only here can scholars find editorials from the 1870s on the latest political controversies, advertisements for the latest fashions, articles on the latest sporting events, and languid poetry from a local artist, all within one source. Newspapers, in short, document more completely the full range of the human experience than nearly any other source available to modern scholars, providing windows into the past available nowhere else. Despite their remarkable value, newspapers have long remained among the most underutilized historical resources. The reason for this paradox is quite simple: the sheer volume and breadth of information available in historical newspapers has, ironically, made it extremely difficult for historians to go through them page-by-page for a given research project. A historian, for example, might need to wade through tens of thousands of newspaper pages in order to answer a single research question (with no guarantee of stumbling onto the necessary information). Recently, both the research potential and problem of scale associated with historical newspapers has expanded greatly due to the rapid digitization of these sources. The National Endowment for the Humanities (NEH) and the Library of Congress (LOC), for example, are sponsoring a nationwide historical digitization project, Chronicling America, geared toward digitizing all surviving historical newspapers in the United States, from 1836 to the present. This project recently digitized its one millionth page (and they project to have more than 20 million pages within a few years), opening a vast wealth of historical newspapers in digital form. While projects such as Chronicling America have indeed increased access to these important sources, they have also increased the problem of scale that have long prevent scholars from using these sources in meaningful ways. Indeed, without tools and methods capable of handling such large datasets – and thus sifting out meaningful patterns embedded within them – scholars find themselves confined to performing only basic word searches across enormous collections. These simple searches can, indeed, find stray information scattered in unlikely 46 places. Such rudimentary search tools, however, become increasingly less useful to researchers as datasets continue to grow in size. If a search for a particular term yields 4,000,000 results, even those search results produce a dataset far too large for any single scholar to analyze in a meaningful way using traditional methods. The age of abundance, it turns out, can simply overwhelm historical scholars, as the sheer volume of available digitized historical newspapers is beginning to do. In this paper, we explore the use of topic modeling, in an attempt to identify the most important and potentially interesting topics over a given period of time. Thus, instead of asking a historian to look through thousands of newspapers to identify what may be interesting topics, we take a reverse approach, where we first automatically cluster the data into topics, and then provide these automatically identified topics to the historian so she can narrow her scope to focus on the individual patterns in the dataset that are most applicable to her research. Of more utility would be where the modeling would reveal unexpected topics that point towards unusual patterns previously unknown, thus help shaping a scholar’s subsequent research. The topic modeling can be done for any periods of time, which can consist of individual years or can cover several years at a time. In this way, we can see the changes in the discussions and topics of interest over the years. Moreover, pre-filters can also be applied to the data prior to the topic modeling. For instance, since research being done in the History department at our institution is concerned with the “U. S. cotton economy,” we can use the same approach to identify the interesting topics mentioned in the news articles that talk about the issue of “cotton.” 2 Topic Modeling Topic models have been used by Newman and Block (2006) and Nelson (2010) on newspaper corpora to discover topics and trends over time. The former used the probabilistic latent semantic analysis (pLSA) model, and the latter used the latent Dirichlet allocation (LDA) model, a method introduced by Blei et al. (2003). LDA has also been used by Griffiths and Steyvers (2004) to find research topic trends by looking at abstracts of scientific papers. Hall et al. (2008) have similarly applied LDA to discover trends in the computational linguistics field. Both pLSA and LDA models are probabilistic models that look at each document as a mixture of multinomials or topics. The models decompose the document collection into groups of words representing the main topics. See for instance Table 1, which shows two topics extracted from our collection. Topic worth price black white goods yard silk made ladies wool lot inch week sale prices pair suits fine quality state states bill united people men general law government party made president today washington war committee country public york Table 1: Example of two topic groups Boyd-Graber et al. (2009) compared several topic models, including LDA, correlated topic model (CTM), and probabilistic latent semantic indexing (pLSI), and found that LDA generally worked comparably well or better than the other two at predicting topics that match topics picked by the human annotators. We therefore chose to use a parallel threaded SparseLDA implementation to conduct the topic modeling, namely UMass Amherst’s MAchine Learning for LanguagE Toolkit (MALLET) (McCallum, 2002). MALLET’s topic modeling toolkit has been used by Walker et al. (2010) to test the effects of noisy optical character recognition (OCR) data on LDA. It has been used by Nelson (2010) to mine topics from the Civil War era newspaper Dispatch, and it has also been used by Blevins (2010) to examine general topics and to identify emotional moments from Martha Ballards Diary. 3 Dataset Our sample data comes from a collection of digitized historical newspapers, consisting of newspapers published in Texas from 1829 to 2008. Issues are segmented by pages with continuous text containing articles and advertisements. Table 2 provides more information about the dataset. 2 http://mallet.cs.umass.edu/ 1 http://americanpast.richmond.edu/dispatch/ 3 http://historying.org/2010/04/01/ 47 Property Number of titles Number of years Number of issues Number of pages Number of tokens 114 180 32,745 232,567 816,190,453 Table 2: Properties of the newspaper collection 3.1 Sample Years and Categories From the wide range available, we sampled several historically significant dates in order to evaluate topic modeling. These dates were chosen for their unique characteristics (detailed below), which made it possible for a professional historian to examine and evaluate the relevancy of the results. These are the subcategories we chose as samples: • Newspapers from 1865-1901: During this period, Texans rebuilt their society in the aftermath of the American Civil War. With the abolition of slavery in 1865, Texans (both black and white) looked to rebuild their post-war economy by investing heavily in cotton production throughout the state. Cotton was considered a safe investment, and so Texans produced enough during this period to make Texas the largest cotton producer in the United States by 1901. Yet overproduction during that same period impoverished Texas farmers by driving down the market price for cotton, and thus a large percentage went bankrupt and lost their lands (over 50 percent by 1900). As a result, angry cotton farmers in Texas during the 1890s joined a new political party, the Populists, whose goal was to use the national government to improve the economic conditions of farmers. This effort failed by 1896, although it represented one of the largest third-party political revolts in American history. This period, then, was dominated by the rise of cotton as the foundation of the Texas economy, the financial failures of Texas farmers, and their unsuccessful political protests of the 1890s as cotton bankrupted people across the state. These are the issues we would expect to emerge as important topics from newspapers in this category. This dataset consists of 52,555 pages over 5,902 issues. • Newspapers from 1892: This was the year of the formation of the Populist Party, which a large portion of Texas farmers joined for the U. S. presidential election of 1892. The Populists sought to have the U. S. federal government become actively involved in regulating the economy in places like Texas (something never done before) in order to prevent cotton farmers from going further into debt. In the 1892 election, the Populists did surprisingly well (garnering about 10 percent of the vote nationally) and won a full 23 percent of the vote in Texas. This dataset consists of 1,303 pages over 223 issue

ÐHandwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the on-line case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered. Index TermsÐHandwriting recognition, on-line, off-line, written language, signature verification, cursive script, handwriting learning tools, writer authentification.

In this paper, we propose a machine learning-based NLP system for automatically creating animated storyboards using the action descriptions of movie scripts. We focus particularly on the importance of verb semantics when generating graphics commands, and find that semantic role labelling boosts performance and is relatively robust to the effects of

While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they must achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships as well as difficulty in updating the ontology as knowledge changes. Methodologies developed in the fields of Natural Language Processing, information extraction, information retrieval and machine learning provide techniques for automating the enrichment of an ontology from free-text documents. In this article, we review existing methodologies and developed systems, and discuss how existing methods can benefit the development of biomedical ontologies.

This paper provides a thorough evaluation of a set of six important Arabic OCR systems available in the market; namely: Abbyy FineReader, Leadtools, Readiris, Sakhr, Tesseract and NovoVerus. We test the OCR systems using a randomly selected images from the well known Arabic Printed Text Image database (250 images from the APTI database) and using a set of 8 images from an Arabic book. The APTI database contains 45.313.600 of both decomposable and non-decomposable word images. In the evaluation, we conduct two tests. The first test is based on usual metrics used in the literature. In the second test, we provide a novel measure for Arabic language, which can be used for other non-Latin languages.


Question: Tracing linguistic connections in the victorious and defeated factions of explicit opposing clusters.

This paper outlines a novel methodology for automatically extracting hyponymy relationships from large, unrestricted text corpora, with the dual objectives of minimizing reliance on pre-encoded knowledge and ensuring broad applicability across diverse text genres. Hyponymy, a lexical relationship denoting a specific type of a more general category, is crucial for enriching linguistic resources and enhancing various natural language processing tasks. The proposed approach focuses on identifying a set of lexicosyntactic patterns that are both easily detectable and ubiquitously present in text, regardless of genre. These patterns unequivocally signal the presence of the hyponymy relationship. The paper elaborates on the process for discovering these patterns, suggesting that this methodology can be extended to acquire other lexical relationships as well. An initial implementation of the acquisition algorithm is presented, with the results used to both augment and critically evaluate the structure of a large, manually curated thesaurus. The paper concludes by exploring potential extensions and applications of this work in areas such as information retrieval and other NLP domains, highlighting the versatility and impact of the proposed method for enhancing linguistic analysis and resource creation.

Paste the appropriate copyright statement here. ACM now supports three different copyright statements: • ACM copyright: ACM holds the copyright on the work. This is the historical approach. • License: The author(s) retain copyright, but ACM receives an exclusive publication license. • Open Access: The author(s) wish to pay for the work to be open access. The additional fee must be paid to ACM. This text field is large enough to hold the appropriate release statement assuming it is single spaced in a sans-serif 7 point font. Every submission will be assigned their own unique DOI string to be included here. Abstract Twitch.tv, a streaming platform known for video game content, has grown tremendously since its inception in 2011. We examine communication practices in Twitch chats for the popular game Hearthstone, comparing massive chats with at least 10,000 concurrent viewers and small chats with fewer than 2,000 concurrent viewers. Due to the large scale and fast pace of massive chats, communication patterns no longer follow models developed in previous studies of computer-mediated communication. Rather than what other studies have described as communication breakdowns and information overload, participants in massive chats communicate in what we call “crowdspeak.”

Language acquisition in both natural and artificial language learning settings crucially depends on extracting information from ordered sequences. A shared sequence learning mechanism is thus assumed to underlie both natural and artificial language learning. A growing body of empirical evidence is consistent with this hypothesis. By means of artificial language learning experiments, we may therefore gain more insight in this shared mechanism. In this paper, we review empirical evidence from artificial language learning and computational modeling studies, as well as natural language data, and suggest that there are two key factors that help determine processing complexity in sequence learning, and thus in natural language processing. We propose that the specific ordering of non-adjacent dependencies (i.e. nested or crossed), as well as the number of non-adjacent dependencies to be resolved simultaneously (i.e. two or three) are important factors in gaining more insight into the boundaries of human sequence learning; and thus, also in natural language processing. The implications for theories of linguistic competence are discussed.

Word Sense Disambiguation (WSD) is a key enabling-technology. Supervised WSD techniques are the best performing in public evaluations, but need large amounts of hand-tagging data. Existing hand-annotated corpora like SemCor (Miller et al., 1993), which is annotated with WordNet senses (Fellbaum, 1998) allow for a small improvement over the simple most frequent sense heuristic, as attested in the all-words track of the last Senseval competition (Snyder and Palmer, 2004). In theory, larger amounts of training data (SemCor has approx. 500M words) would improve the performance of supervised WSD, but no current project exists to provide such an expensive resource. Another problem of the supervised approach is that the inventory and distribution of senses changes dramatically from one domain to the other, requiring additional hand-tagging of corpora (Martı́nez and Agirre, 2000; Koeling et al., 2005). Supervised WSD is based on the “fixed-list of senses” paradigm, where the senses for a target word are a closed list coming from a dictionary or lexicon. Lexicographers and semanticists have long warned about the problems of such an approach, where senses are listed separately as discrete entities, and have argued in favor of more complex representations, where, for instance, senses are dense regions in a continuum (Cruse, 2000). Unsupervised Word Sense Induction and Discrimination (WSID, also known as corpus-based unsupervised systems) has followed this line of thinking, and tries to induce word senses directly from the corpus. Typical WSID systems involve clustering techniques, which group together similar examples. Given a set of induced clusters (which represent word uses or senses1), each new occurrence of the target word will be compared to the clusters and the most similar cluster will be selected as its sense. One of the problems of unsupervised systems is that of managing to do a fair evaluation. Most of current unsupervised systems are evaluated in-house, with a brief comparison to a re-implementation of a former system, leading to a proliferation of unsupervised systems with little ground to compare among them. The goal of this task is to allow for comparison across sense-induction and discrimination systems, and also to compare these systems to other supervised and knowledge-based systems. The paper is organizes as follows. Section 2 presents the evaluation framework used in this task. Section 3 presents the systems that participated in the task, and the official results. Finally, Section 4 draws the conclusions.

Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at http://github.com/UKPLab/ coling2018-xling_argument_mining.

Many languages have a grammatical gender system whereby all nouns are assigned a gender (most commonly feminine, masculine, or neuter). Two studies examined whether (1) the assignment of genders to nouns is truly arbitrary (as has been claimed), and (2) whether the grammatical genders assigned to nouns have semantic consequences. In the first study, English speakers’ intuitions about the genders of animals (but not artifacts) were found to correlate with the grammatical genders assigned to the names of these objects in Spanish and German. These findings suggest that the assignment of genders to nouns is not entirely arbitrary but may to some extent reflect the perceived masculine or feminine properties of the nouns’ referents. Results of the second study suggested that people’s ideas about the genders of objects are strongly influenced by the grammatical genders assigned to these objects in their native language. Spanish and German speakers’ memory for object--name pairs (e.g., apple--Patricia) was better for pairs where the gender of the proper name was congruent with the grammatical gender of the object name (in their native language), than when the two genders were incongruent. This was true even though both groups performed the task in English. These results suggest that grammatical gender may not be as arbitrary or as purely grammatical as was previously

Vector-space word representations have been very successful in recent years at improving performance across a variety of NLP tasks. However, common to most existing work, words are regarded as independent entities without any explicit relationship among morphologically related words being modeled. As a result, rare and complex words are often poorly estimated, and all unknown words are represented in a rather crude way using only one or a few vectors. This paper addresses this shortcoming by proposing a novel model that is capable of building representations for morphologically complex words from their morphemes. We combine recursive neural networks (RNNs), where each morpheme is a basic unit, with neural language models (NLMs) to consider contextual information in learning morphologicallyaware word representations. Our learned models outperform existing word representations by a good margin on word similarity tasks across many datasets, including a new dataset we introduce focused on rare words to complement existing ones in an interesting way.

Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list. Unfortunately, there is no obvious alternative, no other simple way for lexicographers to keep track of what has been done or for readers to find the word they are looking for. But a frequent objection to this solution is that finding things on an alphabetical list can be tedious and time-consuming. Many people who would like to refer to a dictionary decide not to bother with it because finding the information would interrupt their work and break their train of thought.

Many research studies adopt manually selected patterns for semantic relation extraction. However, manually identifying and discovering patterns is time consuming and it is difficult to discover all potential candidates. Instead, we propose an automatic pattern construction approach to extract verb synonyms and antonyms from English newspapers. Instead of relying on a single pattern, we combine results indicated by multiple patterns to maximize the recall.

This study reports the results of using minimum description length (MDL) analysis to model unsupervised learning of the morphological segmentation of European languages, using corpora ranging in size from 5,000 words to 500,000 words. We develop a set of heuristics that rapidly develop a probabilistic morphological grammar, and use MDL as our primary tool to determine whether the modifications proposed by the heuristics will be adopted or not. The resulting grammar matches well the analysis that would be developed by a human morphologist. In the final section, we discuss the relationship of this style of MDL grammatical analysis to the notion of evaluation metric in early generative grammar.

The strength with which a statement is made can have a significant impact on the audience. For example, international relations can be strained by how the media in one country describes an event in another; and papers can be rejected because they overstate or understate their findings. It is thus important to understand the effects of statement strength. A first step is to be able to distinguish between strong and weak statements. However, even this problem is understudied, partly due to a lack of data. Since strength is inherently relative, revisions of texts that make claims are a natural source of data on strength differences. In this paper, we introduce a corpus of sentence-level revisions from academic writing. We also describe insights gained from our annotation efforts for this task.


Question: Tracing linguistic connections in the victorious and defeated factions of explicit opposing clusters.

This paper introduces an innovative multimodal sentiment analysis framework, combining convolutional neural networks (CNNs) with multiple kernel learning (MKL) to decode user emotions and opinions from video content. The proliferation of the Social Web has facilitated the widespread sharing of ideas and opinions, leading to a vast increase in multimodal content, particularly videos, from various sources including computers, smartphones, tablets, and surveillance cameras. To address the challenge of extracting meaningful insights from this abundance of data, the proposed framework integrates visual, audio, and textual modalities using MKL, enhancing the accuracy of sentiment analysis. Compared to the current leading model in the field, this new approach demonstrates a significant improvement, achieving a 10-13% and 3-5% higher accuracy in polarity detection and emotion recognition, respectively. Additionally, the paper offers an in-depth exploration of decision-level fusion, underscoring the effectiveness of the ensemble application of CNNs and MKL for multimodal sentiment analysis.

The words people use in their daily lives can reveal important aspects of their social and psychological worlds. With advances in computer technology, text analysis allows researchers to reliably and quickly assess features of what people say as well as subtleties in their linguistic styles. Following a brief review of several text analysis programs, we summarize some of the evidence that links natural word use to personality, social and situational fluctuations, and psychological interventions. Of particular interest are findings that point to the psychological value of studying particles-parts of speech that include pronouns, articles, prepositions, conjunctives, and auxiliary verbs. Particles, which serve as the glue that holds nouns and regular verbs together, can serve as markers of emotional state, social identity, and cognitive styles.

The use of distributional language representations have opened new paths in solving a variety of NLP problems. However, alternative approaches can take advantage of information unavailable through pure statistical means. This paper presents a method for building vector representations from meaning unit blocks called concept definitions, which are obtained by extracting information from a curated linguistic resource (Wiktionary). The representations obtained in this way can be compared through conventional cosine similarity and are also interpretable by humans. Evaluation was conducted in semantic similarity and relatedness test sets, with results indicating a performance comparable to other methods based on single linguistic resource extraction. The results also indicate noticeable performance gains when combining distributional similarity scores with the ones obtained using this approach. Additionally, a discussion on the proposed method’s shortcomings is provided in the analysis of error cases.

Automatic lexical acquisition has been an active area of research in computational linguistics for over two decades, but the automatic identification of new word-senses has received attention only very recently. Previous work on this topic has been limited by the availability of appropriate evaluation resources. In this paper we present the largest corpus-based dataset of diachronic sense differences to date, which we believe will encourage further work in this area. We then describe several extensions to a state-of-the-art topic modelling approach for identifying new word-senses. This adapted method shows superior performance on our dataset of two different corpus pairs to that of the original method for both: (a) types having taken on a novel sense over time; and (b) the token instances of such novel senses.

Subjectivity taggingis distinguishing sentences used to present opinions and evaluations from sentences used to objectively present factual information. There are numerous applications for which subjectivity tagging is relevant, including information extraction and information retrieval. This paper identifies strong clues of subjectivity using the results of a method for clustering words according to distributional similarity (Lin 1998), seeded by a small amount of detailed manual annotation. These features are then further refined with the addition of lexical semantic features of adjectives, specifically polarityandgradability(Hatzivassiloglou & McKeown 1997), which can be automatically learned from corpora. In 10-fold cross validation experiments, features based on both similarity clusters and the lexical semantic features are shown to have higher precision than features based on each alone.

In languages that use diacritical characters, if these special signs are stripped-off from a word, the resulted string of characters may not exist in the language, and therefore its normative form is, in general, easy to recover. However, this is not always the case, as presence or absence of a diacritical sign attached to a base letter of a word which exists in both variants, may change its grammatical properties or even the meaning, making the recovery of the missing diacritics a difficult task, not only for a program but sometimes even for a human reader. We describe and evaluate an accurate knowledge-based system for automatic recovering the missing diacritics in MSOffice documents written in Romanian. For the rare cases when the system is not able to reliably make a decision, it either provides the user a list of words with their recovery suggestions, or probabilistically choose one of the possible changes, but leaves a trace (a highlighted comment) on each word the modification of which was uncertain.

Whispered speech is a natural mode of speech in which voicing is absent – its acoustics differ significantly from normally spoken speech or so-called neutral speech, such that it is challenging to use only neutral speech to build speech processing and automatic recognition systems that can deal effectively with whisper. At the same time, humans can naturally produce and perceive whispered speech without explicit training. Tonal languages such as Mandarin present an interesting dilemma – tone is primarily encoded by pitch tracks which are absent during whispered speech, but humans can still tell tones apart. How humans manage to process whispered speech well without explicit training on it, whereas machine algorithms fail, is presently an unresolved question which could prove fruitful with study. This, however, is hindered by the lack of suitable, systematically collected corpora. We present iWhisper-Mandarin, a 25-hour parallel corpus of neutral and whispered Mandarin, designed to support research in linguistics and speech technology. We demonstrate and verify that earlier techniques applied to whispered speech from non-tonal languages also work with Mandarin, and present some preliminary studies on voice activity detection and whispered Mandarin speech recognition.

We consider the task of predicting lexical entailment using distributional vectors. We focus experiments on one previous classifier which was shown to only learn to detect prototypicality of a word pair. Analysis shows that the model single-mindedly learns to detect Hearst Patterns, which are well known to be predictive of lexical relations. We present a new model which exploits this Hearst Detector functionality, matching or outperforming prior work on multiple data sets.

Word embeddings have become widelyused in document analysis. While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain can be achieved over traditional approaches based on bag-of-words. In this paper, we propose a new document clustering approach by combining any word embedding with a state-of-the-art algorithm for clustering empirical distributions. By using the Wasserstein distance between distributions, the word-to-word semantic relationship is taken into account in a principled way. The new clustering method is easy to use and consistently outperforms other methods on a variety of data sets. More importantly, the method provides an effective framework for determining when and how much word embeddings contribute to document analysis. Experimental results with multiple embedding models are reported.

Recent years have brought a symbolic growth in the volume of research in Sentiment Analysis, mostly on highly subjective text types like movie or product reviews. The main difference these texts have with news articles is that their target is apparently defined and unique across the text. Thence while dealing with news articles, we performed three subtasks namely identifying the target; separation of good and bad news content from the good and bad sentiment expressed on the target and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. On concluding these tasks, we present our work on mining opinions about three different Indian political parties during elections in the year 2009. We built a Corpus of 689 opinion-rich instances from three different English dailies namely The Hindu, Times of India and Economic Times extracted from 02/ 01/ 2009 to 05/ 01/ 2009 (MM/ DD/ YY). In which (a) we tested the relative suitability of various sentiment analysis methods (both machine learning and lexical based) and (b) we attempted to separate positive or negative opinion from good or bad news. Evaluation includes comparison of three sentiment analysis methods (two machine learning based and one lexical based) and analyzing the choice of certain words used in political text which influence the Sentiments of public in polls. This preliminary experiment will benefit in predicting and forecasting the winning party in forthcoming Indian elections 2014.

In this paper, we provide several alternatives to the classical Bag-Of-Words model for automatic authorship attribution. To this end, we consider linguistic and writing style information such as grammatical structures to construct different document representations. Furthermore we describe two techniques to combine the obtained representations: combination vectors and ensemble based meta classification. Our experiments show the viability of our approach.