1. Articles in category: Entailment

    25-48 of 51 « 1 2 3 »
    1. NL from Logic: Connecting Entailment and Generation

      The paper introduces an index of fully-specified logical form for unrestricted natural language. The index is argued to facilitate both computation of entailment and generation under semantic control. Content Type Book ChapterDOI 10.1007/978-3-642-14287-1_10Authors Crit Cremers, Leiden University Centre for Linguistics Book Series Lecture Notes in Computer ScienceOnline ISSN 1611-3349Print ISSN 0302-9743 Book Series Volume Volume 6042/2010 Book Logic, Language and MeaningDOI 10.1007/978-3-642-14287-1Print ISBN 978-3-642-14286-4
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      Mentions: Leiden
    2. Using Machine Translation Systems to Expand a Corpus in Textual Entailment

      This paper explores how to increase the size of Textual Entailment Corpus by using Machine Translation systems to generate additional 〈t,h 〉 pairs. We also analyze the theoretical upper bound of a Corpus expanded by machine translation systems, and propose how it computes the confidence of a classification translator-based RTE system. At the end, we show an algorithm to expand the corpus size using Translator engines and we provide some results over a real RTE system. Content Type Book ChapterDOI 10.1007/978-3-642-14770-8_12Authors Julio J. Castillo, National University of Cordoba-FaMAF, Cordoba, Argentina Book Series Lecture Notes in Computer ScienceOnline ISSN ...
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      Mentions: Argentina Cordoba RTE
    3. A Syntactic Textual Entailment System Based on Dependency Parser

      The development of a syntactic textual entailment system that compares the dependency relations in both the text and the hypothesis has been reported. The Stanford Dependency Parser has been run on the 2-way RTE-3 development set and the dependency relations obtained for a text and hypothesis pair has been compared. Some of the important comparisons are: subject-subject comparison, subject-verb comparison, object-verb comparison and cross subject-verb comparison. Corresponding verbs are further compared using the WordNet. Each of the matches is assigned some weight learnt from the development corpus. A threshold has been set on the fraction of matching hypothesis relations based ...
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      Mentions: Partha Pakray
    4. A Survey of Paraphrasing and Textual Entailment Methods. (arXiv:0912.3747v1 [cs.CL])

      Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often very similar. Both kinds of methods are useful in a wide range of natural language processing applications, including question answering, summarization, text generation, and ...
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    5. Measuring machine translation quality as semantic equivalence: A metric based on entailment features

      Abstract  Current evaluation metrics for machine translation have increasing difficulty in distinguishing good from merely fair translations. We believe the main problem to be their inability to properly capture meaning: A good translation candidate means the same thing as the reference translation, regardless of formulation. We propose a metric that assesses the quality of MT output through its semantic equivalence to the reference translation, based on a rich set of match and mismatch features motivated by textual entailment. We first evaluate this metric in an evaluation setting against a combination metric of four state-of-the-art scores. Our metric predicts human judgments ...
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    6. Towards Extensible Textual Entailment Engines: The Edits Package

      This paper presents the first release of EDITS, an open-source software package for recognizing Textual Entailment developed by FBK-irst. The main contributions of EDITS consist in: i) providing a basic framework for a distance-based approach to the task, ii) providing a highly customizable environment to experiment with different algorithms, iii) allowing for easy extensions and integrations with new algorithms and resources. System’s main features are described, together with experiments over different datasets showing its potential in terms of tuning and adaptation capabilities. Content Type Book ChapterDOI 10.1007/978-3-642-10291-2_32Authors Matteo Negri, FBK-Irst Trento ItalyMilen Kouylekov, FBK-Irst Trento ItalyBernardo Magnini ...
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    7. Identification of Sentence-to-Sentence Relations Using a Textual Entailer

      Abstract  We show in this article how an approach developed for the task of recognizing textual entailment relations can be extended to identify paraphrase and elaboration relations. Entailment is a unidirectional relation between two sentences in which one sentence logically infers the other. There seems to be a close relation between entailment and two other sentence-to-sentence relations: elaboration and paraphrase. This close relation is discussed to theoretically justify the newly derived approaches. The proposed approaches use lexical, syntactic, and shallow negation handling. The proposed approaches offer significantly better results than several baselines. When compared to other paraphrase and elaboration approaches ...
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    8. Information Synthesis for Answer Validation

      This paper proposes an integration of Recognizing Textual Entailment (RTE) with other additional information to deal with the Answer Validation task. The additional information used in our participation in the Answer Validation Exercise (AVE 2008) is from named-entity (NE) recognizer, question analysis component, etc. We have submitted two runs, one run for English and the other for German, achieving f-measures of 0.64 and 0.61 respectively. Compared with our system last year, which purely depends on the output of the RTE system, the extra information does show its effectiveness. Content Type Book ChapterDOI 10.1007/978-3-642-04447-2_57Authors Rui Wang, Saarland ...
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    9. Without a 'doubt'? Unsupervised discovery of downward-entailing operators. (arXiv:0906.2415v1 [cs.CL])

      An important part of textual inference is making deductions involving monotonicity, that is, determining whether a given assertion entails restrictions or relaxations of that assertion. For instance, the statement 'We know the epidemic spread quickly' does not entail 'We know the epidemic spread quickly via fleas', but 'We doubt the epidemic spread quickly' entails 'We doubt the epidemic spread quickly via fleas'. Here, we present the first algorithm for the challenging lexical-semantics problem of learning linguistic constructions that, like 'doubt', are downward entailing (DE). Our algorithm is unsupervised, resource-lean, and effective, accurately recovering many DE operators that are missing from ...
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    10. Automatic Frame Extraction from Sentences

      We present a method for automatic extraction of frames from a dependency graph. Our method uses machine learning applied to a dependency tree to identify frames and assign frame elements. The system is evaluated by cross-validation on FrameNet sentences, and also on the test data from the SemEval 2007 task 19. Our system is intended for use in natural language processing applications such as summarization, entailment, and novelty detection. Content Type Book ChapterDOI 10.1007/978-3-642-01818-3_13Authors Martin Scaiano, University of Ottawa School of Information Technology and EngineeringDiana Inkpen, University of Ottawa School of Information Technology and Engineering Book Series Lecture ...
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    11. Semantic relation mining of solid compounds in medical corpora.

      Related Articles Semantic relation mining of solid compounds in medical corpora. Stud Health Technol Inform. 2008;136:217-22 Authors: Kokkinakis D In the context of scientific and technical texts, meaning is usually embedded in noun compounds and the semantic interpretation of these compounds deals with the detection and semantic classification of the relation that holds between the compound's constituents. Semantic relation mining, the technology applied for marking up, interpreting, extracting and classifying relations that hold between pairs of words, is an important enterprise that contribute to deeper means of enhancing document understanding technologies, such as Information Extraction, Question Answering ...
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    12. Improving Question Answering Tasks by Textual Entailment Recognition

      This paper explores a suitable way to integrate a Textual Entailment (TE) system, which detects unidirectional semantic inferences, into Question Answering (QA) tasks. We propose using TE as an answer validation engine to improve QA systems, and we evaluate its performance using the Answer Validation Exercise framework. Results point out that our TE system can improve the QA task considerably. Content Type Book ChapterDOI 10.1007/978-3-540-69858-6_37Authors Óscar Ferrández, University of Alicante Natural Language Processing and Information Systems Group Department of Computing Languages and SystemsRafael Muñoz, University of Alicante Natural Language Processing and Information Systems Group Department of Computing Languages ...
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    13. Systems, methods, and software for hyperlinking names

      Hyperlinking or associating documents to other documents based on the names of people in the documents has become more desirable. Although there is an automated system for installing such hyperlinks into judicial opinions, the system is not generally applicable to other types of names and documents, nor well suited to determine hyperlinks for names that might refer to two or more similarly named persons. Accordingly, the inventor devised systems, methods, and software that facilitate hyperlinking names in documents, regardless of type. One exemplary system includes a descriptor module and a linking module. The descriptor module develops descriptive patterns for selecting ...
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    14. Detecting Expected Answer Relations through Textual Entailment

      This paper presents a novel approach to Question Answering over structured data, which is based on Textual Entailment recognition. The main idea is that the QA problem can be recast as an entailment problem, where the text (T) is the question and the hypothesis (H) is a relational pattern, which is associated to “instructions” for retrieving the answer to the question. In this framework, given a question Q and a set of answer patterns P, the basic operation is to select those patterns in P that are entailed by Q. We report on a number of experiments which show the ...
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    15. Memory-Efficient Inference in Relational Domains

      Memory-Efficient Inference in Relational Domains Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350, U.S.A. {parag, pedrod}@cs.washington.edu Parag Singla Pedro Domingos Abstract Propositionalization of a first-order theory followed by satisfiability testing has proved to be a remarkably efficient approach to inference in relational domains such as planning (Kautz & Selman 1996) and verification (Jackson 2000). More recently, weighted satisfiability solve
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    25-48 of 51 « 1 2 3 »
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