1. Articles in category: Entailment

    25-48 of 55 « 1 2 3 »
    1. Towards Better Ontological Support for Recognizing Textual Entailment

      Many applications in modern information technology utilize ontological knowledge to increase their performance, precision, and success rate. However, the integration of ontological sources is in general a difficult task since the semantics of all concepts, individuals, and relations must be preserved across the various sources. In this paper we discuss the importance of combined background knowledge for recognizing textual entailment (RTE). We present and analyze formally a new graph-based procedure for integration of concepts and individuals from ontologies based on the hierarchy of WordNet. We embed it in our experimental RTE framework where a deep-shallow semantic text analysis combined with ...
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      Mentions: Germany WordNet RTE
    2. Using Sentence Semantic Similarity Based on WordNet in Recognizing Textual Entailment

      This paper presents a Recognizing Textual Entailment system which uses semantic distances to sentence level over WordNet to assess the impact on predicting Textual Entailment datasets. We extent word-to-word metrics to sentence level in order to best fit in textual entailment domain. Finally, we show experiments over several RTE datasets and draw conclusions about the useful of WordNet semantic measures on this task. As a conclusion, we show that an initial but average-score system can be built using only semantic information from WordNet. Content Type Book ChapterDOI 10.1007/978-3-642-16952-6_37Authors Julio J. Castillo, National University of Cordoba-FaMAF, Cordoba, ArgentinaMarina E ...
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    3. A Semantic Oriented Approach to Textual Entailment Using WordNet-Based Measures

      In this paper, we present a Recognizing Textual Entailment system which uses semantic similarity metrics to sentence level only using WordNet as source of knowledge. We show how the widely used semantic measures WordNet-based can be generalized to build sentence level semantic metrics in order to be used in the RTE. We also provide an analysis of efficiency of these metrics and drawn some conclusions about their utility in the practice in recognizing textual entailment. We also show that using the proposed method to extend word semantic measures could be used in building an average score system that only uses ...
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      Mentions: Argentina Cordoba RTE
    4. Recognizing Textual Entailment Using a Machine Learning Approach

      We present our experiments on Recognizing Textual Entailment based on modeling the entailment relation as a classification problem. As features used to classify the entailment pairs we use a symmetric similarity measure and a non-symmetric similarity measure. Our system achieved an accuracy of 66% on the RTE-3 development dataset (with 10-fold cross validation) and accuracy of 63% on the RTE-3 test dataset. Content Type Book ChapterDOI 10.1007/978-3-642-16773-7_15Authors Miguel Angel Ríos Gaona, Center for Computing Research, National Polytechnic Institute, MexicoAlexander Gelbukh, Center for Computing Research, National Polytechnic Institute, MexicoSivaji Bandyopadhyay, Computer Science & Engineering Department, Jadavpur University, Kolkata, 700 032 ...
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    5. 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
    6. 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
    7. 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
    8. 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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    9. 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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    10. 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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    11. 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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    12. 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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    13. 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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    14. 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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    15. 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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    25-48 of 55 « 1 2 3 »
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