Question: Applying Markov logic to machine reading tasks.

This paper presents an innovative A* parsing algorithm designed to significantly expedite the process of finding the exact Viterbi parse, achieving this by prudently estimating outside Viterbi probabilities. We explore various estimation techniques and outline efficient procedures for their computation. When applied to sentences from the Penn Treebank, our most sophisticated estimation method decreases the total number of edges processed to less than 3% of what exhaustive parsing would require. Even a simpler, pre-computable estimate, which can be calculated in under a minute, still results in a five-fold reduction in computational work. The A* parsing technique adapts the traditional A* graph search procedure to a specific hypergraph associated with parsing tasks. Unlike best-first and finite-beam methods, which offer speed-ups at the cost of accuracy, the A* parser ensures the retrieval of the most probable parse, not just an approximation. The algorithm is compatible with a broad spectrum of parser control strategies and sustains a worst-case cubic time complexity, ensuring both speed and reliability in parse selection.

We present the components of the ColumbiaGWU contribution to the 2016 TAC KBP BeSt Evaluation.

Reiter and Dale’s book is about natural language generation (NLG), and describes methods for building systems of this kind. It offers a comprehensive overview of the field of NLG, from a perspective that has rarely been described elsewhere. It is very valuable for people with little or even no knowledge about NLG particulars who intend to investigate applications where NLG technology might play a certain role, as well as for people who want to obtain an application-oriented overview of the field. In this sense, all communities intended to be served by the book—students, academics in related areas, and software developers—are well addressed. In addition, some sections provide material that is probably new even to researchers with considerable experience in the field, such as the discussions on corpus analysis and domain modeling. In the introduction, the field of NLG is briefly characterized from researchand application-oriented perspectives and is illustrated by screen shots produced by several systems. Then conditions for effective uses of this technology are elaborated and contrasted with conditions where other techniques are more appropriate, and methods for determining the intended functionality of a system to be built are discussed. The main sections of the book are devoted to the prototypical architecture of applicationoriented NLG systems and their major processing phases: document planning, microplanning, and surface realization. Each of these three phases is illustrated by a number of detailed examples, demonstrating the successive refinements of utterance specifications in the course of processing. In the final section, the embedding of natural language processing technology is discussed, featuring typography, combined uses with graphics and hypertext, and integration with speech. The methods are illustrated by a large number of examples—the book contains more than 120 figures on its 248 pages. At the end of each section, a number of useful references for further reading are related to the section topics. In the appendix, a table summarizing the 35 systems referred to in the book is given. In general, the presentations in the book are beneficial for their high degree of detail in documenting representations. Running examples are illustrated by intermediate representations at various stages of processing. They demonstrate very well the increasing degrees of precision in which utterances are specified and the associated commitments about possible text portions that express these specifications. Particularly valuable are the comparisons of alternative representations, ranging from skeletal propositions over lexicalized case frames to canned text, including a comparison of the consequences for the distribution of work over the system components involved and discussions of the associated pros and cons. These chapters provide a number of valuable hints, given the currently low degree of standardization in the field. Further important aspects addressed, which have been widely ignored in the literature so far,

Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far. In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be adapted to cover state-of-the-art statistical relational learning approaches.

MOTIVATION
The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications.

RESULTS
We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.

Prediction without justification has limited applicability. As a remedy, we learn to extract pieces of input text as justifications – rationales – that are tailored to be short and coherent, yet sufficient for making the same prediction. Our approach combines two modular components, generator and encoder, which are trained to operate well together. The generator specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction. Rationales are never given during training. Instead, the model is regularized by desiderata for rationales. We evaluate the approach on multi-aspect sentiment analysis against manually annotated test cases. Our approach outperforms attention-based baseline by a significant margin. We also successfully illustrate the method on the question retrieval task.1

Memory-Based Learning (MBL) is based on the storage of all available training data, and similarity-based reasoning for handling new cases. By interpreting tasks such as POS tagging and shallow parsing as classification tasks, the advantages of MBL (implicit smoothing of sparse data, automatic integration and relevance weighting of information sources, handling exceptional data) contribute to state-of-the-art accuracy. However, Hidden Markov Models (HMM) typically achieve higher accuracy than MBL (and other Machine Learning approaches) for tasks such as POS tagging and chunking. In this paper, we investigate how the advantages of MBL, such as its potential to integrate various sources of information, come to play when we compare our approach to HMMs on two Information Extraction (IE) datasets: the well-known Seminar Announcement data set and a new German Curriculum Vitae data set. 1 Memory-Based Language Processing Memory-Based Learning (MBL) is a supervised classification-based learning method. A vector of feature values (an instance) is associated with a class by a classifier that lazily extrapolates from the most similar set (nearest neighbors) selected from all stored training examples. This is in contrast to eager learning methods like decision tree learning [26], rule induction [9], or Inductive Logic Programming [7], which abstract a generalized structure from the training set beforehand (forgetting the examples themselves), and use that to derive a classification for a new instance. In MBL, a distance metric on the feature space defines what are the nearest neighbors of an instance. Metrics with feature weights based on information-theory or other relevance statistics allow us to use rich representations of instances and their context, and to balance the influences of diverse information sources in computing distance. Natural Language Processing (NLP) tasks typically concern the mapping of an input representation (e.g., a series of words) into an output representation (e.g., the POS tags corresponding to each word in the input). Most NLP tasks can therefore easily be interpreted as sequences of classification 34 Zavrel, Daelemans tasks: e.g., given a word and some representation of its context, decide what tag to assign to each word in its context. By creating a separate classification instance (a “moving window” approach) for each word and its context, shallow syntactic or semantic structures can be produced for whole sentences or texts. In this paper, we argue that more semantic and complex input-output mappings, such as Information Extraction, can also effectively be modeled by such a Memory-based classification-oriented framework, and that this approach has a number of very interesting advantages over rivalling methods, most notably that each classification decision can be made dependent on a very rich and diverse set of features. The properties of MBL as a lazy, similarity-based learning method seem make a good fit to the properties of typical disambiguation problems in NLP: • Similar input representations lead to similar output. E.g., words occurring in a similar context in general have the same POS tag. Similarity-based reasoning is the core of MBL. • Many sub-generalizations and exceptions. By keeping in memory all training instances, exceptions included, an MBL approach can capture generalization from exceptional or low-frequency cases according to [12]. • Need for integration of diverse types of information. E.g., in Information Extraction, lexical features, spelling features, syntactic as well as phrasal context features, global text structure, and layout features can potentially be very relevant. • Automatic smoothing in very rich event spaces. Supervised learning of NLP tasks regularly runs into problems of sparse data; not enough training data is available to extract reliable parameters for complex models. MBL incorporates an implicit robust form of smoothing by similarity [33]. In the remainder of this Section, we will show how a memory-, similarity-, and classification-based approach can be applied to shallow syntactic parsing, and can lead to state-of-the-art accuracy. Most of the tasks discussed here can also easily be modeled using Hidden Markov Models (HMM), and often with surprising accuracy. We will discuss the strengths of the HMMs and draw a comparison between the classification-based MBL method and the sequence-optimizing HMM approach (Section 1.2). 1.1 Memory-Based Shallow Parsing Shallow parsing is an important component of most text analysis systems in Text Mining applications such as information extraction, summary generation, and question answering. It includes discovering the main constituents of sentences (NPs, VPs, PPs) and their heads, and determining syntactic relationships like subject, object, adjunct relations between verbs and heads of other constituents. This is an important first step to understanding the who, what, when, and where of sentences in a text. Feature-Rich Memory-Based Classification for Information Extraction 35 In our approach to memory-based shallow parsing, we carve up the syntactic analysis process into a number of classification tasks with input vectors representing a focus item and a dynamically selected surrounding context. These classification tasks can be segmentation tasks (e.g., decide whether a focus word or tag is the start or end of an NP) or disambiguation tasks (e.g., decide whether a chunk is the subject NP, the object NP or neither). Output of some memory-based modules is used as input by other memory-based modules (e.g., a tagger feeds a chunker and the latter feeds a syntactic relation assignment module). Similar ideas about cascading of processing steps have also been explored in other approaches to text analysis: e.g., finite state partial parsing [1,18], statistical decision tree parsing [23], and maximum entropy parsing [30]. The approach briefly described here is explained and evaluated in more detail in [10,11,6] . Chunking The phrase chunking task can be defined as a classification task by generalizing the approach of [28], who proposed to convert NP-chunking to tagging each word with I for a word inside an NP, O for outside an NP, and B for between two NPs). The decision on these so called IOB tags for a word can be made by looking at the Part-of-Speech tag and the identity of the focus word and its local context. For the more general task of chunking other non-recursive phrases, we simply extend the tag set with IOB tags for each type of phrase. To illustrate this encoding with the extended IOB tag set, we can tag the sentence: But/CC [NP the/DT dollar/NN NP] [ADVP later/RB ADVP] [VP rebounded/VBD VP] ,/, [VP finishing/VBG VP] [ADJP slightly/RB higher/R ADJP] [Prep against/IN Prep] [NP the/DT yen/NNS NP] [ADJP although/IN ADJP] [ADJP slightly/RB lower/JJR ADJP] [Prep against/IN Prep] [NP the/DT mark/NN NP] ./. as: But/CCO the/DTI−NP dollar/NNI−NP later/RBI−ADV P rebounded/VBDI−V P ,/,O finishing/VBGI−V P slightly/RBI−ADV P higher/RBRI−ADV P against/INI−Prep the/DTI−NP yen/NNSI−NP although/INI−ADJP slightly/RBB−ADJP lower/JJRI−ADJP against/INI−Prep the/DTI−NP mark/NNI−NP ./.O Table 1 (from [6]) shows the accuracy of this memory-based chunking approach when training and testing on Wall Street Journal material. We report Precision, Recall, and Fβ=1 scores, a weighted harmonic mean of Recall and Precision (Fβ = (β+1)∗P∗R β2∗P+R) ). 1 An online demonstration of the Memory-Based Shallow Parser can be found at http://ilk.kub.nl . 36 Zavrel, Daelemans type precision recall Fβ=1 NPchunks 92.5 92.2 92.3 VPchunks 91.9 91.7 91.8 ADJPchunks 68.4 65.0 66.7 ADVPchunks 78.0 77.9 77.9 Prepchunks 95.5 96.7 96.1 PPchunks 91.9 92.2 92.0 ADVFUNCs 78.0 69.5 73.5 Table 1. Results of chunking–labeling experiments. Reproduced from [6]. Grammatical Relation Finding After POS tagging, phrase chunking and labeling, the last step of the shallow parsing consists of resolving the (types of) attachment between labeled phrases. This is done by using a classifier to assign a grammatical relation (GR) between pairs of words in a sentence. In our approach, one of these words is always a verb, since this yields the most important GRs. The other word (focus) is the head of the phrase which is annotated with this grammatical relation in the treebank (e.g., a noun as head of an NP). An instance for such a pair of words is constructed by extracting a set of feature values from the sentence. The instance contains information about the verb and the focus: a feature for the word form and a feature for the POS of both. It also has similar features for the local context of the focus. Experiments on the training data suggest an optimal context width of two words to the left and one to the right. In addition to the lexical and the local context information, superficial information about clause structure was included: the distance from the verb to the focus, counted in words. A negative distance means that the focus is to the left of the verb. Other features contain the number of other verbs between the verb and the focus, and the number of intervening commas. These features were chosen by manual “feature engineering”. Table 2 shows some of the feature-value instances corresponding to the following sentence (POS tags after the slash, chunks denoted with square and curly brackets, and adverbial functions after the dash): [ADVP Not /RB surprisingly /RB ADVP] ,/, [NP Peter /NNP Miller /NNP NP] ,/, [NP who /WP NP] [VP organized /VBD VP] [NP the /DT conference /NN NP] {PP-LOC [Prep in /IN Prep] [NP New /NNP York /NNP NP] PP-LOC} ,/, [VP does /VBZ not /RB want /VB to /TO come /VB VP] {PP-DIR [Prep to /IN Prep] [NP Paris /NNP NP] PP-DIR} [Prep without /IN Prep] [VP bringing /VBG VP] [NP his /PRP$ wife /NN NP]. Table 3 shows the results of the experiments. In the first row, only POS tag features are used. Other rows show the results when adding several types of chunk informati

We describe SLING, a framework for parsing natural language into semantic frames. SLING supports general transition-based, neural-network parsing with bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. The parsing model is trained end-to-end using only the text tokens as input. The transition system has been designed to output frame graphs directly without any intervening symbolic representation. The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast inference during parsing. SLING is implemented in C++ and it is available for download on GitHub.

Recently, a number of authors have proposed treating dialog ue systems as Markov decision processes (MDPs). However, the practical applica tion of MDP algorithms to dialogue systems faces a number of severe technical chall enges. We have built a general software tool (RLDS, for Reinforcement Learning fo r Dialogue Systems) based on the MDP framework, and have applied it to dialogue co rpora gathered from two dialogue systems built at AT&T Labs. Our experiment s demonstrate that RLDS holds promise as a tool for “browsing” and understandin g correlations in complex, temporally dependent dialogue corpora.

In natural-language discourse, related events tend to appear near each other to describe a larger scenario. Such structures can be formalized by the notion of a frame (a.k.a. template), which comprises a set of related events and prototypical participants and event transitions. Identifying frames is a prerequisite for information extraction and natural language generation, and is usually done manually. Methods for inducing frames have been proposed recently, but they typically use ad hoc procedures and are difficult to diagnose or extend. In this paper, we propose the first probabilistic approach to frame induction, which incorporates frames, events, participants as latent topics and learns those frame and event transitions that best explain the text. The number of frames is inferred by a novel application of a split-merge method from syntactic parsing. In end-to-end evaluations from text to induced frames and extracted facts, our method produced state-of-the-art results while substantially reducing engineering effort.

We present an approach for extracting relations between named entities from natural language documents. The approach is based solely on shallow linguistic processing, such as tokenization, sentence splitting, part-of-speech tagging, and lemmatization. It uses a combination of kernel functions to integrate two different information sources: (i) the whole sentence where the relation appears, and (ii) the local contexts around the interacting entities. We present the results of experiments on extracting five different types of relations from a dataset of newswire documents and show that each information source provides a useful contribution to the recognition task. Usually the combined kernel significantly increases the precision with respect to the basic kernels, sometimes at the cost of a slightly lower recall. Moreover, we performed a set of experiments to assess the influence of the accuracy of named-entity recognition on the performance of the relation-extraction algorithm. Such experiments were performed using both the correct named entities (i.e., those manually annotated in the corpus) and the noisy named entities (i.e., those produced by a machine learning-based named-entity recognizer). The results show that our approach significantly improves the previous results obtained on the same dataset.


Question: Markov logic applied to machine reading tasks.

In the realm of computational linguistics, a recurring query is: "Which resources should I consult to gain proficiency in statistical natural language processing (NLP)?" Traditionally, the response has involved recommending a combination of probability theory and information theory texts, augmented by a selection of research papers from conferences and journals. The absence of a dedicated NLP resource has often left learners dissatisfied. Thus, the publication of Eugene Charniak's "Statistical Language Learning" was met with anticipation. However, upon reading the book, one's feelings about its contribution to the expanding field of statistical NLP are likely to be ambivalent.

The book commences with a concise overview of the traditional artificial intelligence methods in NLP (chapter 1), encompassing morphology, syntax, semantics, and pragmatics. It then provides a cursory treatment of probability theory and information theory fundamentals (chapter 2). Subsequently, it delves into the intricacies of hidden Markov models (chapters 3-4) and probabilistic context-free grammars (chapters 5-6). The final section of the book explores advanced statistical language learning topics, including grammar induction (chapter 7), syntactic disambiguation (chapter 8), word clustering (chapter 9), and word sense disambiguation (chapter 10).

On the positive side, "Statistical Language Learning" offers an engaging and accessible introduction to statistical modeling in NLP. Its writing style is captivating and it effectively caters to readers with a limited mathematical background. The book presents a well-curated selection of statistical NLP topics, making it a valuable starting point for beginners. Moreover, the explanations of the forward-backward algorithm for hidden Markov models and the inside-outside algorithm for probabilistic context-free grammars are particularly clear and easy to grasp.

Nonetheless, the book's efficacy as a comprehensive guide for aspiring researchers in the field of statistical NLP is questionable. The author's intentions, as stated in the preface, are ambitious. However, the book falls short of providing the depth and breadth required to fully equip readers with the knowledge and skills necessary to engage in cutting-edge research. The lack of detailed mathematical derivations and advanced algorithmic concepts limits its utility as a primary learning resource for those seeking to delve deeply into statistical NLP methodologies.

Many machine learning applications require the ability to learn from and reason about noisy multi-relational data. To address this, several effective representations have been developed that provide both a language for expressing the structural regularities of a domain, and principled support for probabilistic inference. In addition to these two aspects, however, many applications also involve a third aspect–the need to reason about similarities–which has not been directly supported in existing frameworks. This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reasoning about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domainspecific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learning techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.

Many Knowledge Bases (KBs) are now readily available and encompass colossal quantities of information thanks to either a long-term funding effort (e.g. WordNet, OpenCyc) or a collaborative process (e.g. Freebase, DBpedia). However, each of them is based on a different rigid symbolic framework which makes it hard to use their data in other systems. It is unfortunate because such rich structured knowledge might lead to a huge leap forward in many other areas of AI like natural language processing (word-sense disambiguation, natural language understanding, ...), vision (scene classification, image semantic annotation, ...) or collaborative filtering. In this paper, we present a learning process based on an innovative neural network architecture designed to embed any of these symbolic representations into a more flexible continuous vector space in which the original knowledge is kept and enhanced. These learnt embeddings would allow data from any KB to be easily used in recent machine learning methods for prediction and information retrieval. We illustrate our method on WordNet and Freebase and also present a way to adapt it to knowledge extraction from raw text.

Statistical learning problems in many fields involve sequential data. This paper formalizes the principal learning tasks and describes the methods that have been developed within the machine learning research community for addressing these problems. These methods include sliding window methods, recurrent sliding windows, hidden Markov models, conditional random fields, and graph transformer networks. The paper also discusses some open research issues.

Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language-processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure.

Manually querying search engines in order to accumulate a large bodyof factual information is a tedious, error-prone process of piecemealsearch. Search engines retrieve and rank potentially relevantdocuments for human perusal, but do not extract facts, assessconfidence, or fuse information from multiple documents. This paperintroduces KnowItAll, a system that aims to automate the tedious process ofextracting large collections of facts from the web in an autonomous,domain-independent, and scalable manner.The paper describes preliminary experiments in which an instance of KnowItAll, running for four days on a single machine, was able to automatically extract 54,753 facts. KnowItAll associates a probability with each fact enabling it to trade off precision and recall. The paper analyzes KnowItAll's architecture and reports on lessons learned for the design of large-scale information extraction systems.

Traditional relation extraction predicts relations within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, existing structured sources of the same schema. The need for existing datasets can be avoided by using a universal schema: the union of all involved schemas (surface form predicates as in OpenIE, and relations in the schemas of preexisting databases). This schema has an almost unlimited set of relations (due to surface forms), and supports integration with existing structured data (through the relation types of existing databases). To populate a database of such schema we present matrix factorization models that learn latent feature vectors for entity tuples and relations. We show that such latent models achieve substantially higher accuracy than a traditional classification approach. More importantly, by operating simultaneously on relations observed in text and in pre-existing structured DBs such as Freebase, we are able to reason about unstructured and structured data in mutually-supporting ways. By doing so our approach outperforms stateof-the-art distant supervision.

The amount of readily available on-line text has reached hundreds of billions of words and continues to grow. Yet for most core natural language tasks, algorithms continue to be optimized, tested and compared after training on corpora consisting of only one million words or less. In this paper, we evaluate the performance of different learning methods on a prototypical natural language disambiguation task, confusion set disambiguation, when trained on orders of magnitude more labeled data than has previously been used. We are fortunate that for this particular application, correctly labeled training data is free. Since this will often not be the case, we examine methods for effectively exploiting very large corpora when labeled data comes at a cost.

Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We explore the use of Amazon’s Mechanical Turk system, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the Web. We investigate five tasks: affect recognition, word similarity, recognizing textual entailment, event temporal ordering, and word sense disambiguation. For all five, we show high agreement between Mechanical Turk non-expert annotations and existing gold standard labels provided by expert labelers. For the task of affect recognition, we also show that using non-expert labels for training machine learning algorithms can be as effective as using gold standard annotations from experts. We propose a technique for bias correction that significantly improves annotation quality on two tasks. We conclude that many large labeling tasks can be effectively designed and carried out in this method at a fraction of the usual expense.

The management of uncertainty is crucial when harvesting structured content from unstructured and noisy sources. Knowledge Graphs (KGs) are a prominent example. KGs maintain both numerical and non-numerical facts, with the support of an underlying schema. These facts are usually accompanied by a confidence score that witnesses how likely is for them to hold. Despite their popularity, most of existing KGs focus on static data thus impeding the availability of timewise knowledge. What is missing is a comprehensive solution for the management of uncertain and temporal data in KGs. The goal of this paper is to fill this gap. We rely on two main ingredients. The first is a numerical extension of Markov Logic Networks (MLNs) that provide the necessary underpinning to formalize the syntax and semantics of uncertain temporal KGs. The second is a set of Datalog constraints with inequalities that extend the underlying schema of the KGs and help to detect inconsistencies. From a theoretical point of view, we discuss the complexity of two important classes of queries for uncertain temporal KGs: maximum a-posteriori and conditional probability inference. Due to the hardness of these problems and the fact that MLN solvers do not scale well, we also explore the usage of Probabilistic Soft Logics (PSL) as a practical tool to support our reasoning tasks. We report on an experimental evaluation comparing the MLN and PSL approaches.

Nilsson, N.J., Logic and artificial intelligence, Artificial Intelligence 47 (1990) 31-56. The theoretical foundations of the logical approach to artificial intelligence are presented. Logical languages are widely used for expressing the declarative knowledge needed in artificial intelligence systems. Symbolic logic also provides a clear semantics for knowledge representation languages and a methodology for analyzing and comparing deductive inference techniques. Several observations gained from experience with the approach are discussed. Finally, we confront some challenging problems for artificial intelligence and describe what is being done in an attempt to solve them.


Question: Applying Markov logic to machine reading tasks.

This document details the outcomes of the PASCAL Network of Excellence's inaugural Recognising Textual Entailment (RTE-1) Challenge, a benchmark aimed at assessing systems' ability to determine if the meaning of one text fragment can be logically deduced from another, regardless of context. The RTE task is posited as encapsulating the fundamental inferences about the flexibility of semantic expression, which are typically required across a wide range of applications. The RTE-1 Challenge garnered significant interest within the academic community, drawing 17 entries from a variety of research groups, which underscores the broad relevance of the task at hand.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: An Adaptive Differential Evolution Algorithm for Solving Economic Dispatch Problems with Valve-Point Effects
This paper presents an Adaptive Differential Evolution (ADE) algorithm to solve the economic dispatch problem with valve-point effects. The valve-point effect is a nonlinear characteristic of the generator cost function, which is often modeled as a series of quadratic functions with sinusoidal terms. The ADE algorithm is a variant of the Differential Evolution (DE) algorithm, which is a population-based optimization algorithm that has been successfully applied to various optimization problems. The ADE algorithm adapts the mutation and crossover strategies of DE to improve its convergence and search efficiency. The proposed ADE algorithm is tested on several benchmark systems with valve-point effects, and the results are compared with other optimization algorithms. The results show that the ADE algorithm can effectively solve the economic dispatch problem with valve-point effects and achieve better solutions than other algorithms.

**Similar Documents**: This article introduces an Adaptive Differential Evolution (ADE) algorithm as a solution to the economic dispatch problem, specifically when dealing with valve-point effects. Valve-point effects introduce a nonlinear complexity to the generator cost function, typically modeled using a sequence of quadratic functions augmented with sinusoidal components. ADE is an enhanced version of the Differential Evolution (DE) algorithm, a population-based optimization technique that has proven effective in tackling a variety of optimization challenges. By dynamically adjusting the mutation and crossover mechanisms of DE, ADE enhances its convergence properties and search efficiency. The efficacy of the ADE algorithm is demonstrated through its application to a number of standard benchmark systems featuring valve-point effects. Comparative analysis with alternative optimization methods reveals that ADE not only adeptly handles the economic dispatch problem with valve-point effects but also outperforms competing algorithms in terms of solution quality.

Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., “My house is bigger than me.” However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, “Tyler entered his house” implies that his house is bigger than Tyler. In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.

To answer the question in machine comprehension (MC) task, the models need to establish the interaction between the question and the context. To tackle the problem that the single-pass model cannot reflect on and correct its answer, we present Ruminating Reader. Ruminating Reader adds a second pass of attention and a novel information fusion component to the Bi-Directional Attention Flow model (BIDAF). We propose novel layer structures that construct a query aware context vector representation and fuse encoding representation with intermediate representation on top of BIDAF model. We show that a multi-hop attention mechanism can be applied to a bi-directional attention structure. In experiments on SQuAD, we find that the Reader outperforms the BIDAF baseline by 2.1 F1 score and 2.7 EM score. Our analysis shows that different hops of the attention have different responsibilities in selecting answers.

We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-ofthe-art models. While most successful approaches for reading comprehension rely on recurrent neural networks (RNNs), running them over long documents is prohibitively slow because it is difficult to parallelize over sequences. Inspired by how people first skim the document, identify relevant parts, and carefully read these parts to produce an answer, we combine a coarse, fast model for selecting relevant sentences and a more expensive RNN for producing the answer from those sentences. We treat sentence selection as a latent variable trained jointly from the answer only using reinforcement learning. Experiments demonstrate the state of the art performance on a challenging subset of the WIKIREADING dataset (Hewlett et al., 2016) and on a new dataset, while speeding up the model by 3.5x-6.7x.

Conventional QA systems cannot answer to the questions composed of two or more sentences. Therefore, we aim to construct a QA system that can answer such multiple-sentence questions. As the first stage, we propose a method for classifying multiple-sentence questions into question types. Specifically, we first extract the core sentence from a given question text. We use the core sentence and its question focus in question classification. The result of experiments shows that the proposed method improves F-measure by 8.8% and accuracy by 4.4%.

MOTIVATION
The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications.

RESULTS
We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging. Publication info: Proceedings of EMNLP 2002, pp. 79–86.

Over the past decade, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. A typical fMRI experiment can produce a three-dimensional image related to the human subject's brain activity every half second, at a spatial resolution of a few millimeters. As in other modern empirical sciences, this new instrumentation has led to a flood of new data, and a corresponding need for new data analysis methods. We describe recent research applying machine learning methods to the problem of classifying the cognitive state of a human subject based on fRMI data observed over a single time interval. In particular, we present case studies in which we have successfully trained classifiers to distinguish cognitive states such as (1) whether the human subject is looking at a picture or a sentence, (2) whether the subject is reading an ambiguous or non-ambiguous sentence, and (3) whether the word the subject is viewing is a word describing food, people, buildings, etc. This learning problem provides an interesting case study of classifier learning from extremely high dimensional (105 features), extremely sparse (tens of training examples), noisy data. This paper summarizes the results obtained in these three case studies, as well as lessons learned about how to successfully apply machine learning methods to train classifiers in such settings.

Recognizing Textual Entailment (RTE) plays an important role in NLP applications like question answering, information retrieval, etc. Most previous works either use classifiers to employ elaborately designed features and lexical similarity or bring distant supervision and reasoning technique into RTE task. However, these approaches are hard to generalize due to the complexity of feature engineering and are prone to cascading errors and data sparsity problems. For alleviating the above problems, some work use LSTM-based recurrent neural network with word-by-word attention to recognize textual entailment. Nevertheless, these work did not make full use of knowledge base (KB) to help reasoning. In this paper, we propose a deep neural network architecture called Multi-task Knowledge Assisted LSTM (MKAL), which aims to conduct implicit inference with the assistant of KB and use predicate-topredicate attention to detect the entailment between predicates. In addition, our model applies a multi-task architecture to further improve the performance. The experimental results show that our proposed method achieves a competitive result compared to the previous work.

We describe a system for extracting complex events among genes and proteins from biomedical literature, developed in context of the BioNLP’09 Shared Task on Event Extraction. For each event, its text trigger, class, and arguments are extracted. In contrast to the prevailing approaches in the domain, events can be arguments of other events, resulting in a nested structure that better captures the underlying biological statements. We divide the task into independent steps which we approach as machine learning problems. We define a wide array of features and in particular make extensive use of dependency parse graphs. A rule-based post-processing step is used to refine the output in accordance with the restrictions of the extraction task. In the shared task evaluation, the system achieved an F-score of 51.95% on the primary task, the best performance among the participants.

We propose a methodology for a novel type of discourse annotation whose model is tuned to the analysis of a text as narrative. This is intended to be the basis of a “story bank” resource that would facilitate the automatic analysis of narrative structure and content. The methodology calls for annotators to construct propositions that approximate a reference text, by selecting predicates and arguments from among controlled vocabularies drawn from resources such as WordNet and VerbNet. Annotators then integrate the propositions into a conceptual graph that maps out the entire discourse; the edges represent temporal, causal and other relationships at the level of story content. Because annotators must identify the recurring objects and themes that appear in the text, they also perform coreference resolution and word sense disambiguation as they encode propositions. We describe a collection experiment and a method for determining inter-annotator agreement when multiple annotators encode the same short story. Finally, we describe ongoing work toward extending the method to integrate the annotator’s interpretations of character agency (the goals, plans and beliefs that are relevant, yet not explictly stated in the text).


Question: Utilizing Markov logic in the context of machine reading tasks.

This paper introduces an enhanced framework for adaptive duplicate detection in databases, focusing on refining data cleaning and integration procedures. Traditionally, duplicate record identification has been handled using generic or manually calibrated distance metrics to gauge record similarity. Here, we advocate for an innovative approach that utilizes trainable text similarity metrics, tailored to the unique requirements of each database field. By implementing learnable text distance functions, we demonstrate their capacity to adapt to the specific similarity criteria pertinent to the field's context. Two distinct learnable text similarity metrics are proposed for this purpose: an expanded version of the learnable string edit distance and a groundbreaking vector-space based measure that leverages Support Vector Machines (SVMs) for training. Our empirical findings, derived from a variety of datasets, confirm the superiority of this framework in enhancing duplicate detection accuracy compared to conventional methods.

Traditional relation extraction predicts relations within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, existing structured sources of the same schema. The need for existing datasets can be avoided by using a universal schema: the union of all involved schemas (surface form predicates as in OpenIE, and relations in the schemas of preexisting databases). This schema has an almost unlimited set of relations (due to surface forms), and supports integration with existing structured data (through the relation types of existing databases). To populate a database of such schema we present matrix factorization models that learn latent feature vectors for entity tuples and relations. We show that such latent models achieve substantially higher accuracy than a traditional classification approach. More importantly, by operating simultaneously on relations observed in text and in pre-existing structured DBs such as Freebase, we are able to reason about unstructured and structured data in mutually-supporting ways. By doing so our approach outperforms stateof-the-art distant supervision.

To answer the question in machine comprehension (MC) task, the models need to establish the interaction between the question and the context. To tackle the problem that the single-pass model cannot reflect on and correct its answer, we present Ruminating Reader. Ruminating Reader adds a second pass of attention and a novel information fusion component to the Bi-Directional Attention Flow model (BIDAF). We propose novel layer structures that construct a query aware context vector representation and fuse encoding representation with intermediate representation on top of BIDAF model. We show that a multi-hop attention mechanism can be applied to a bi-directional attention structure. In experiments on SQuAD, we find that the Reader outperforms the BIDAF baseline by 2.1 F1 score and 2.7 EM score. Our analysis shows that different hops of the attention have different responsibilities in selecting answers.

One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then, the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this article, we provide an example of using world knowledge for domain-dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then, we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features.
 A preliminary version of this work appeared in the proceedings of KDD 2015 [Wang et al. 2015a]. This journal version has made several major improvements. First, we have proposed a new and general learning framework for machine learning with world knowledge as indirect supervision, where document clustering is a special case in the original paper. Second, in order to make our unsupervised semantic parsing method more understandable, we add several real cases from the original sentences to the resulting logic forms with all the necessary information. Third, we add details of the three semantic filtering methods and conduct deep analysis of the three semantic filters, by using case studies to show why the conceptualization-based semantic filter can produce more accurate indirect supervision. Finally, in addition to the experiment on 20 newsgroup data and Freebase, we have extended the experiments on clustering results by using all the combinations of text (20 newsgroup, MCAT, CCAT, ECAT) and world knowledge sources (Freebase, YAGO2).

Although computers excel at certain bounded tasks that are difficult for humans, such as solving integrals, they have difficulty performing commonsense tasks that are easy for humans, such as understanding stories. In this Technical Forum contribution, we discuss commonsense reasoning and what makes it difficult for computers. We contend that commonsense reasoning is too hard a problem to solve using any single artificial intelligence technique. We propose a multilevel architecture consisting of diverse reasoning and representation techniques that collaborate and reflect in order to allow the best techniques to be used for the many situations that arise in commonsense reasoning. We present story understanding—specifically, understanding and answering questions about progressively harder children’s texts—as a task for evaluating and scaling up a commonsense reasoning system.

Probabilities describe degrees of belief, and probabilistic inference describes rational reasoning under uncertainty. It is no wonder, then, that probabilistic models have exploded onto the scene of modern artificial intelligence, cognitive science, and applied statistics: these are all sciences of inference under uncertainty. But as probabilistic models have become more sophisticated, the tools to formally describe them and to perform probabilistic inference have wrestled with new complexity. Just as programming beyond the simplest algorithms requires tools for abstraction and composition, complex probabilistic modeling requires new progress in model representation—probabilistic programming languages. These languages provide compositional means for describing complex probability distributions; implementations of these languages provide generic inference engines: tools for performing efficient probabilistic inference over an arbitrary program. In their simplest form, probabilistic programming languages extend a well-specified deterministic programming language with primitive constructs for random choice. This is a relatively old idea, with foundational work by Giry, Kozen, Jones, Moggi, SahebDjahromi, Plotkin, and others [see e.g. 7]. Yet it has seen a resurgence thanks to new tools for probabilistic inference and new complexity of probabilistic modeling applications. There are a number of recent probabilistic programming languages [e.g. 8, 9, 11–17], embodying different tradeoffs in expressivity, efficiency, and perspicuity. We will focus on the probabilistic programming language Church [6] for simplicity, but the design of probabilistic languages to best support complex model representation and efficient inference is an active and important topic. Church extends (the purely functional subset of) Scheme with elementary random primitives, such as flip (a bernoulli), multinomial, and gaussian. In addition, Church includes language constructs that simplify modeling. For instance, mem, a higher-order procedure that memoizes its input function, is useful for describing persistent random properties and lazy model construction. (Interestingly, memoization has a semantic effect in probabilistic languages.) If we view the semantics of the underlying deterministic language as a map from programs to executions of the program, the semantics of the probabilistic language will be a map from programs to distributions over executions. When the program halts

In this paper, we describe the integration of Natural Language Processing (NLP) within an emotional planner to support Interactive Storytelling. Our emotional planner is based on a standard HSP planner, whose originality is drawn from altering the agents’ beliefs and emotional states. Each character is driven by its own planner, while characters are able to operate on their reciprocal feelings thus affecting each other. Our baseline story is constituted by a classic XIX century French novel from Gustave Flaubert in which characters feelings play a dominant role. This approach benefits from the fact that Flaubert has described a specific ontology for his characters feelings. The objective of NLP should be to uncover from natural language utterances the same kind of affective elements, which requires an integration between NLP and the planning component at the level of semantic content. This research is illustrated with examples from a first fully integrated prototype comprising NLP, emotional planning and real-time 3D animation.

This tutorial gives an overview on state-of-the-art methods for the automatic construction of large knowledge bases and harnessing them for data and text analytics. It covers both big-data methods for building knowledge bases and knowledge bases being assets for big-data applications. The tutorial also points out challenges and research opportunities. 1. MOTIVATION AND SCOPE Comphrehensive machine-readable knowledge bases (KB’s) have been pursued since the seminal projects Cyc [19, 20] and WordNet [12]. In contrast to these manually created KB’s, great advances have recently been made on automating the building and curation of large KB’s [1, 16], using information extraction (IE) techniques and harnessing highquality Web sources like Wikipedia. Prominent endeavors of this kind include academic research projects such as DBpedia [3], KnowItAll [10], NELL [5] and YAGO [29], as well as industrial ones such as Freebase. These projects provide automatically constructed KB’s of facts about named entities, their semantic classes, and their mutual relationships. They contain millions of entities and billions of facts about them. Moreover, several KB’s are interlinked at the entity level, forming the backbone of the Web of Linked Data [14]. Such world knowledge in turn enables cognitive applications and knowledge-centric services like disambiguating natural-language text, entity linking, text summarization, deep question answering, and semantic search and analytics over entities and relations in Web and enterprise data (e.g., [2, 6, 8, 13]). Prominent examples of how KB’s can be harnessed include the Google Knowledge Graph [27] and the IBM Watson question answering system [17]. This tutorial presents state-of-the-art methods, recent advances, research opportunities, and open challenges along this avenue of knowledge harvesting and its applications. Particular emphasis is on the twofold role of KB’s for bigdata analytics: using scalable distributed algorithms for harvesting knowledge from Web and text sources, and leveraging entity-centric knowledge for deeper interpretation of and This work is licensed under the Creative Commons AttributionNonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/. Obtain permission prior to any use beyond those covered by the license. Contact copyright holder by emailing info@vldb.org. Articles from this volume were invited to present their results at the 40th International Conference on Very Large Data Bases, September 1st 5th 2014, Hangzhou, China. Proceedings of the VLDB Endowment, Vol. 7, No. 13 Copyright 2014 VLDB Endowment 2150-8097/14/08. better intelligence with big data. The following sections outline the structure of the tutorial. An extensive bibliography on this theme is given in [30]. 2. BUILDING KNOWLEDGE BASES Digital Knowledge: Today’s KB’s represent their data mostly in RDF-style SPO (subject-predicate-object) triples. We introduce this data model and the most salient KB projects, which include KnowItAll [10, 11], BabelNet [22], ConceptNet [28], DBpedia [3, 18], DeepDive [24], Freebase [4], ImageNet [7], NELL [5], Wikidata [31], WikiNet [21], WikiTaxonomy [26], and YAGO [29, 15]. We briefly discuss industrial projects like the Google Knowledge Graph and related work at Google [9, 13, 25], the EntityCube and Probase projects at Microsoft Research [23, 32], and IBM’s Watson project [17]. Harvesting Knowledge on Entities and Classes: Every entity in a KB (e.g., Steve Jobs) belongs to one or multiple classes (e.g., computer pioneer, entrepreneur). These classes are organized into a taxonomy, where more special classes are subsumed by more general classes (e.g., person). We discuss two families of methods to harvest such information: Wikipedia-based approaches that analyze the category system, and Web-based approaches that use techniques like set expansion. 3. HARVESTING FACTS AT WEB SCALE Harvesting Relational Facts: Relational facts express properties of and relationships between entities. There is a large spectrum of methods to extract such facts from Web documents. We give an overview on methods from pattern matching (e.g., regular expressions), computational linguistics (e.g., dependency parsing), statistical learning (e.g., factor graphs and MLN’s), and logical consistency reasoning (e.g., weighted MaxSat or ILP solvers). We also discuss to what extent these approaches scale to handle big data. Open Information Extraction: Alternatively to methods that operate on a pre-specified set of relations and entities, open information extraction harvests arbitrary SPO triples from natural language documents. It aggressively taps into noun phrases as entity candidates and verbal phrases as prototypic patterns for relations. We discuss recent methods that follow this direction. Some methods along these lines make clever use of big-data techniques like frequent sequence mining and map-reduce computation. Temporal and Multilingual Knowledge: Properly interpreting entities and facts in a KB often requires additional meta-information like entity names in different languages and the temporal scope of facts. We discuss tech-

We introduce a methodology for automating the maintenance of domain-specific taxonomies based on natural language text understanding. A given ontology is incrementally updated as new concepts are acquired from real-world texts. The acquisition process is centered around the linguistic and conceptual “quality” of various forms of evidence underlying the generation and refinement of concept hypotheses. On the basis of the quality of evidence, concept hypotheses are ranked according to credibility and the most credible ones are selected for assimilation into the domain knowledge base.

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.

Extensive knowledge bases of entailment rules between predicates are crucial for applied semantic inference. In this paper we propose an algorithm that utilizes transitivity constraints to learn a globally-optimal set of entailment rules for typed predicates. We model the task as a graph learning problem and suggest methods that scale the algorithm to larger graphs. We apply the algorithm over a large data set of extracted predicate instances, from which a resource of typed entailment rules has been recently released (Schoenmackers et al., 2010). Our results show that using global transitivity information substantially improves performance over this resource and several baselines, and that our scaling methods allow us to increase the scope of global learning of entailment-rule graphs.


Question: Applying Markov logic to machine reading tasks.

Scaling Textual Reasoning for Web-based Question Answering

The majority of Web-based question and answer (Q/A) systems operate by identifying pages that directly contain the answer to a given question. However, these systems are ineffective when the answer requires inference from multiple sentences, which may be scattered across different pages. To address this limitation, we present the HERCULES system, which employs textual reasoning over tuples extracted from textual data. While prior research on textual inference (TI), such as the studies on textual entailment, has been primarily applied to texts of paragraph length, HERCULES employs knowledge-based model construction to scale TI to a vast corpus of 117 million Web pages. With a processing time of just a few minutes, HERCULES achieves a doubling of recall for exemplar queries in three distinct domains: geography, business, and nutrition. Significantly, the system's runtime is linearly proportional to the size of its input corpus, thanks to an unexpected characteristic of numerous textual relations within the Web corpus—they exhibit "approximate" functionality in a rigorously defined manner.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A Hybrid Methodology for Economic Dispatch with Valve-Point Effect

This paper introduces a hybrid methodology for solving the economic dispatch problem, particularly when dealing with the valve-point effect, a non-smooth characteristic that complicates the optimization process. The proposed approach combines a modified particle swarm optimization (MPSO) algorithm with a local search method, the Nelder-Mead simplex algorithm, to enhance solution accuracy and convergence speed. The MPSO is utilized to explore the solution space and find promising solutions, while the Nelder-Mead simplex algorithm is employed to refine the solutions and ensure they are optimal. The hybrid methodology is tested on a variety of system sizes, ranging from small to large power systems, and the results are compared with those obtained from pure MPSO and other traditional optimization techniques. The comparative analysis demonstrates the superiority of the hybrid methodology in terms of solution quality and computational efficiency.

**Similar Documents**: An Enhanced Hybrid Algorithm for Economic Dispatch with Valve-Point Effect

In this research, an enhanced hybrid algorithm is proposed to tackle the economic dispatch problem, with a particular focus on addressing the valve-point effect, a non-linear feature that introduces complexity to the optimization task. The suggested method integrates an advanced particle swarm optimization (APSO) technique with a local search mechanism, the Nelder-Mead simplex algorithm, to improve the precision and speed of convergence.

A major opportunity for NLP to have a realworld impact is in helping educators score student writing, particularly content-based writing (i.e., the task of automated short answer scoring). A major challenge in this enterprise is that scored responses to a particular question (i.e., labeled data) are valuable for modeling but limited in quantity. Additional information from the scoring guidelines for humans, such as exemplars for each score level and descriptions of key concepts, can also be used. Here, we explore methods for integrating scoring guidelines and labeled responses, and we find that stacked generalization (Wolpert, 1992) improves performance, especially for small training sets.

Traditional relation extraction predicts relations within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, existing structured sources of the same schema. The need for existing datasets can be avoided by using a universal schema: the union of all involved schemas (surface form predicates as in OpenIE, and relations in the schemas of preexisting databases). This schema has an almost unlimited set of relations (due to surface forms), and supports integration with existing structured data (through the relation types of existing databases). To populate a database of such schema we present matrix factorization models that learn latent feature vectors for entity tuples and relations. We show that such latent models achieve substantially higher accuracy than a traditional classification approach. More importantly, by operating simultaneously on relations observed in text and in pre-existing structured DBs such as Freebase, we are able to reason about unstructured and structured data in mutually-supporting ways. By doing so our approach outperforms stateof-the-art distant supervision.

Conventional QA systems cannot answer to the questions composed of two or more sentences. Therefore, we aim to construct a QA system that can answer such multiple-sentence questions. As the first stage, we propose a method for classifying multiple-sentence questions into question types. Specifically, we first extract the core sentence from a given question text. We use the core sentence and its question focus in question classification. The result of experiments shows that the proposed method improves F-measure by 8.8% and accuracy by 4.4%.

In this paper we explore deep learning models with memory component or attention mechanism for question answering task. We combine and compare three models, Neural Machine Translation [1], Neural Turing Machine [5], and Memory Networks [15] for a simulated QA data set [14]. This paper is the first one that uses Neural Machine Translation and Neural Turing Machines for solving QA tasks. Our results suggest that the combination of attention and memory have potential to solve certain QA problem.

Most Web-based Q/A systems work by finding pages that contain an explicit answer to a question. These systems are helpless if the answer has to be inferred from multiple sentences, possibly on different pages. To solve this problem, we introduce the HOLMES system, which utilizes textual inference (TI) over tuples extracted from text. Whereas previous work on TI (e.g., the literature on textual entailment) has been applied to paragraph-sized texts, HOLMES utilizes knowledge-based model construction to scale TI to a corpus of 117 million Web pages. Given only a few minutes, HOLMES doubles recall for example queries in three disparate domains (geography, business, and nutrition). Importantly, HOLMES’s runtime is linear in the size of its input corpus due to a surprising property of many textual relations in the Web corpus—they are “approximately” functional in a well-defined sense.

Multiplicative weight-updating algorithms such as Winnow have been studied extensively in the COLT literature, but only recently have people started to use them in applications. In this paper, we apply a Winnow-based algorithm to a task in natural language: contextsensitive spelling correction. This is the task of xing spelling errors that happen to result in valid words, such as substituting to for too, casual for causal, and so on. Previous approaches to this problem have been statistics-based; we compare Winnow to one of the more successful such approaches, which uses Bayesian classi ers. We nd that: (1) When the standard (heavily-pruned) set of features is used to describe problem instances, Winnow performs comparably to the Bayesian method; (2) When the full (unpruned) set of features is used, Winnow is able to exploit the new features and convincingly outperform Bayes; and (3) When a test set is encountered that is dissimilar to the training set, Winnow is better than Bayes at adapting to the unfamiliar test set, using a strategy we will present for combining learning on the training set with unsupervised learning on the (noisy) test set. In Machine Learning: Proceedings of the 13th International Conference, Lorenza Saitta, ed., Morgan Kaufmann, San Francisco, CA, 1996, pages 182{190. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonpro t educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. Copyright c Mitsubishi Electric Information Technology Center America, 1996 201 Broadway, Cambridge, Massachusetts 02139 Dept. of Appl. Math. & CS, Weizmann Institute of Science, Rehovot 76100, Israel. 1. First printing, TR96-07, April 1996

Recently proposed Story Cloze Test [Mostafazadeh et al., 2016] is a commonsense machine comprehension application to deal with natural language understanding problem. This dataset contains a lot of story tests which require commonsense inference ability. Unfortunately, the training data is almost unsupervised where each context document followed with only one positive sentence that can be inferred from the context. However, in the testing period, we must make inference from two candidate sentences. To tackle this problem, we employ the generative adversarial networks (GANs) to generate fake sentence. We proposed a Conditional GANs (CGANs) in which the generator is conditioned by the context. Our experiments show the advantage of the CGANs in discriminating sentence and achieve state-of-the-art results in commonsense story reading comprehension task compared with previous feature engineering and deep learning methods.

Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. This is a fundamental problem in natural language understanding that provides a broad framework for studying language variability and has a large number of applications. We present a principled approach to this problem that builds on inducing rerepresentations of text snippets into a hierarchical knowledge representation along with a sound inferential mechanism that makes use of it to prove semantic entailment.

This paper describes the results of an implemented computational model that cashes out the belief that metaphor interpretation is grounded in embodied primitives. The speci c task addressed is the interpretation of simple causal narratives in the domains of Politics and Economics. The stories are taken from newspaper articles in these domains. When presented with a preparsed version of these narratives as input, the system described is able to generate commonsense inferences consistent with the input.

In this thesis, we present a novel approach for modeling textual entailment using lexicalsemantic information on the level of predicate-argument structure. To this end, we adopt information provided by the Berkeley FrameNet repository and embed it into an implemented end-to-end system. The two main goals of this thesis are the following: (i) to provide an analysis of the potential contribution of frame semantic information to the recognition textual entailment and (ii) to present a robust system architecture that can serve as basis for future experiments, research, and improvement. Our work was carried out in the context of the textual entailment initiative, which since 2005 has set the stage for the broad investigation of inference in natural-language processing tasks, including empirical evaluation of its coverage and reliability. In short, textual entailment describes inferential relations between (entailing) texts and (entailed) hypotheses as interpreted by typical language users. This pre-theoretic notion captures a natural range of inferences as compared to logical entailment, which has traditionally been used within theoretical approaches to natural language semantics. Various methods for modeling textual entailment have been proposed in the literature, ranging from shallow techniques like lexical overlap to shallow syntactic parsing and the exploitation of WordNet relations. Recently, there has been a move towards more structured meaning representations. In particular, the level of predicate-argument structure has gained much attention, which seems to be a natural and straightforward choice. Predicate-argument structure allows annotating sentences or texts with nuclear meaning representations (“who did what to whom”), which are of obvious relevance for this task. For example, it can account for paraphrases like “Ghosts scare John” vs. “John is scared by ghosts”. In this thesis, we present an approach to textual entailment that is centered around the analysis of predicate-argument structure. It combines LFG grammatical analysis, predicate-argument structure in the FrameNet paradigm, and taxonomic information from WordNet into tripartite graph structures. By way of a declarative graph matching algorithm, the “structural and semantic” similarity of hypotheses and texts is computed and the result is represented as feature vectors. A supervised machine learning architecture trained on entailment corpora is used to check textual entailment for new text/hypothesis pairs. The approach is implemented in the SALSA RTE system, which successfully participated in the second and third RTE challenge. While system performance is on a par with that of comparable systems, the intuitively expected strong positive effect of using FrameNet information has not yet been confirmed. In order to evaluate different system components and to assess the potential contribution of FrameNet information for checking textual entailment, we conducted a number of experiments. For example, with the help of a gold-standard corpus, we


Question: A statistical modeling technique for voice activity detection.

This paper delves into speech enhancement techniques, particularly focusing on the significance of the short-time spectral amplitude (STSA) of the speech signal in auditory perception. We introduce a speech enhancement system that employs an a-minimum mean-square error (MMSE) STSA estimator, and we contrast this system with established methods based on Wiener filtering and the "spectral subtraction" algorithm. The derivation of the MMSE STSA estimator is presented, grounded in the assumption that speech and noise spectral components are statistically independent Gaussian random variables. We conduct a thorough analysis of the proposed STSA estimator's performance and juxtapose it with an STSA estimator derived from the Wiener estimator. Additionally, we investigate the MMSE STSA estimator under conditions of uncertainty regarding signal presence in noisy observations. In synthesizing the enhanced signal, the MMSE STSA estimator is combined with the complex exponential of the noisy phase, which is proven to be the MMSE estimator of the complex exponential of the original phase, without influencing the STSA estimation process. The proposed methodology leads to a substantial decrease in noise and yields enhanced speech with a neutral residual noise characteristic. The computational complexity of the proposed algorithm is comparable to that of other systems within the same class, making it a viable alternative for speech enhancement applications.

Our application requires a keyword spotting system with a small memory footprint, low computational cost, and high precision. To meet these requirements, we propose a simple approach based on deep neural networks. A deep neural network is trained to directly predict the keyword(s) or subword units of the keyword(s) followed by a posterior handling method producing a final confidence score. Keyword recognition results achieve 45% relative improvement with respect to a competitive Hidden Markov Model-based system, while performance in the presence of babble noise shows 39% relative improvement.

This paper presents a time-delayed neural network (TDNN) model that has the capability of learning and predicting the dynamic behavior of nonlinear elements that compose a wireless communication system. This model could help speeding up system deployment by reducing modeling time. This paper presents results of effective application of the TDNN model to an amplifier, part of a wireless transmitter.

 SUST, ICERIE Abstract: Hidden Markov model based various phoneme recognition methods for Bengali language is reviewed. Automatic phoneme recognition for Bengali language using multilayer neural network is reviewed. Usefulness of multilayer neural network over single layer neural network is discussed. Bangla phonetic feature table construction and enhancement for Bengali speech recognition is also discussed. Comparison among these methods is discussed.

In this paper, we present improvements made to the TED-LIUM corpus we released in 2012. These enhancements fall into two categories. First, we describe how we filtered publicly available monolingual data and used it to estimate well-suited language models (LMs), using open-source tools. Then, we describe the process of selection we applied to new acoustic data from TED talks, providing additions to our previously released corpus. Finally, we report some experiments we made around these improvements.

We are interested in the problem of adaptive learning in the context of automatic speech recognition (ASR). In this paper, we propose an active learning algorithm for ASR. Automatic speech recognition systems are trained using human supervision to provide transcriptions of speech utterances. The goal of Active Learning is to minimize the human supervision for training acoustic and language models and to maximize the performance given the transcribed and untranscribed data. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, and then selecting the most informative ones with respect to a given cost function for a human to label. In this paper we describe how to estimate the confidence score for each utterance through an on-line algorithm using the lattice output of a speech recognizer. The utterance scores are filtered through the informativeness function and an optimal subset of training samples is selected. The active learning algorithm has been applied to both batch and on-line learning scheme and we have experimented with different selective sampling algorithms. Our experiments show that by using active learning the amount of labeled data needed for a given word accuracy can be reduced by more than 60% with respect to random sampling.

This paper compares the discriminating powers of various acoustic group features for the task of automatic sleepiness recognition using three different classifiers: Voted Perceptron, Simple Logistic, and Random Forest. Interspeech 2011 Sleepiness Sub-Challenge’s “Sleepy Language Corpus” (SLC) is used to generate the 4368 acoustic features of the official baseline feature set. The feature space is divided into Low-Level Descriptor (LLD) partitions. We consider the resulting feature space in groups rather than individually. A group feature corresponds to a set of one or more LLD partitions. The relevance of various group features to sleepiness state is then evaluated using the mentioned classifiers. Employing larger feature sets has been shown to increase the classification accuracy in sleepiness classification. Our results, however, demonstrate that a much smaller subset of the baseline feature set outperforms the official Sub-Challenge baseline on the SLC test data.

In this paper, the detection of shock wave that generated by supersonic bullet is considered. A wavelet based multi-scale products method has been widely used for detection. However, the performance of method decreased at low signal-to-noise ratio (SNR). It is noted that the method does not consider the distribution of the signal and noise. Thus we analyze the method under the standard likelihood ratio test in this paper. It is found that the multi-scale product method is made in an assumption that is extremely restricted, just hold for a special noise condition. Based on the analysis, a general condition is considered for the detection. An improved detector under the standard likelihood ratio test is proposed. Monte Carlo simulations is conducted with simulated shock waves under additive white Gaussian noise. The result shows that this new detection algorithm outperforms the conventional detection algorithm.

A method for studying speech rhythm is presented, using Fourier analysis of the amplitude envelope of bandpass-filtered speech. Rather than quantifying rhythm with time-domain measurements of interval durations, a frequency-domain representation is used--the rhythm spectrum. This paper describes the method in detail, and discusses approaches to characterizing rhythm with low-frequency spectral information.

We present a soccer video abstraction method based on the analysis of the audio and video streams. This method could be applied to other sports as rugby or american football. The main contribution of this paper is the design of an unsupervised summarization method, and more specifically, the introduction of an efficient detector of excited speech segments. An excited commentary is supposed to correspond to an interesting moment of the game. It is simultaneously characterized by an increase of the pitch (or fundamental frequency) within the voiced segments and an increase of the energy supported by the harmonics of the pitch. The pitch is estimated from the autocorrelation function and its local increases are detected from a multiresolution technique. We introduce a specific energy measure for the voiced segments. A statistical analysis of the energy measures is performed to detect the most excited parts of the speech. A deterministic combination of excited speech detection, dominant color identification and camera motion analysis is then performed in order to discriminate between excited speech sequences of the game and excited speech sequences in commercials or in studio shots included in the processed TV programs.
 The method presented here does not need any learning stage. It has been tested on seven soccer videos for a total duration of almost 20 hours.

The $64,000 question in computational linguistics these days is: “What should I read to learn about statistical natural language processing?” I have been asked this question over and over, and each time I have given basically the same reply: there is no text that addresses this topic directly, and the best one can do is find a good probability-theory textbook and a good information-theory textbook, and supplement those texts with an assortment of conference papers and journal articles. Understanding the disappointment this answer provoked, I was delighted to hear that someone had finally written a bookdirectly addressing this topic. However, after reading Eugene Charniak’s Statistical Language Learning, I have very mixed feelings about the impact this bookmight have on the ever-growing field of statistical NLP. The book begins with a very brief description of the classic artificial intelligence approach toNLP (chapter 1), including morphology, syntax, semantics, and pragmatics. It presents a few definitions from probability theory and information theory (chapter 2), then proceeds to introduce hidden Markov models (chapters 3–4) and probabilistic context-free grammars (chapters 5–6). The book concludes with a few chapters discussing advanced topics in statistical language learning, such as grammar induction (chapter 7), syntactic disambiguation (chapter 8), word clustering (chapter 9), andword sense disambiguation (chapter 10). To its credit, the book serves as an interesting popular discussion of statistical modeling in NLP. It is well-written and entertaining, and very accessible to the reader with a limited mathematical background. It presents a good selection of statistical NLP topics to introduce the reader to the field. And the descriptions of the forward-backward algorithm for hidden Markov models and the inside-outside algorithm for probabilistic context-free grammars are intuitive and easy to follow. However, as a resource for someone interested in entering this area of research, this book falls far short of its author’s goals. These goals are clearly stated in the preface: