Textual Entailment Recognition Based on Dependency Analysis and WordNet

Herrera, Jesús; Peñas, Anselmo; Verdejo, Felisa

doi:10.1007/11736790_13

Cited by 47 publications

(30 citation statements)

References 6 publications

(7 reference statements)

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“…Yasser Arafat and Yaser Arafat). A new version of the system uses the edit distance of Levenshtein [5] to solve this problem taking the idea of [4]: if two entities differ in less than 20%, then there is entailment between these entities. With a new system that includes a best representation of years and the use of the edit distance of Levenshtein a new experiment was accomplished.…”

Section: Discussionmentioning

confidence: 99%

The Effect of Entity Recognition on Answer Validation

Rodrigo

Peñas

Herrera

et al. 2007

Evaluation of Multilingual and Multi-Modal Information Retrieval

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The Answer Validation Exercise (AVE) 2006 is aimed at developing systems able to decide whether the answer of a Question Answering (QA) system is correct or not using textual entailment. The most answers to be validated are given from questions that need entities as responses. The paper presents a system that has only used entities to participate in the AVE 2006. The results of the propose system are better than the ones of a baseline system that always accepts all answers, therefore the use of entities can improve the results of an answer validation system. KeywordsQuestion Answering, Answer Validation, Textual Entailment, Entity Recognition MotivationThe Answer Validation Exercise (AVE) [6] of the Cross Language Evaluation Forum (CLEF) 2006 is aimed at developing systems able to decide whether the answer of a Question Answering (QA) system is correct or not using textual entailment [2]. The test corpus has hypothesis-text pairs where the hypothesis contains the response from a QA system and the text is the snippet given by the system to support its answer. Participant systems must return YES or NO for each hypothesis-text pair to indicate if the text entails the hypothesis or not (i.e. the answer is correct according to the text).The paper shows a participant system that only uses information about entities (numeric expressions, temporal expressions and named entities) in order to study the importance of entities in answer validation.The motivation for this experiment comes from the study of the QA task in the CLEF 2005, where 75% of the questions in Spanish were factoids [7]. The responses to factoid questions contain entities (e.g. person names, locations, numbers, dates...). In this way and following the methodology to build the test corpus in the AVE, the most of the pairs will contain entities. For this reason, we think it is important the study of entities in this task.In this experiment, we have used the hypothesis that in the recognition of textual entailment all the elements in the hypothesis must be entailed by elements of the text. We consider this hypothesis mainly must happen with the entities due to entities contain the main information about the hypothesis-text pairs.

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Section: Discussionmentioning

confidence: 99%

The Effect of Entity Recognition on Answer Validation

Rodrigo

Peñas

Herrera

et al. 2007

Evaluation of Multilingual and Multi-Modal Information Retrieval

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“…1 Since the beginning, many RTE systems have included a module for recognizing lexical entailment (Hickl, Bensley, Williams, Roberts, Rink, and Shi 2006;Herrera, Peñas, and Verdejo 2006). The early RLE modules typically used a symmetric similarity measure, such as the cosine measure (Salton and McGill 1983), the LIN measure (Lin 1998), or a measure based on WordNet (Pedersen, Patwardhan, and Michelizzi 2004), but it was understood that entailment is inherently asymmetric and any symmetric measure can only be a rough approximation (Geffet and Dagan 2005).…”

Section: Related Workmentioning

confidence: 99%

Experiments with three approaches to recognizing lexical entailment

Turney¹,

Mohammad²

2014

Nat. Lang. Eng.

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Inference in natural language often involves recognizing lexical entailment (RLE); that is, identifying whether one word entails another. For example, buy entails own. Two general strategies for RLE have been proposed: One strategy is to manually construct an asymmetric similarity measure for context vectors (directional similarity) and another is to treat RLE as a problem of learning to recognize semantic relations using supervised machine learning techniques (relation classification). In this paper, we experiment with two recent state-of-the-art representatives of the two general strategies. The first approach is an asymmetric similarity measure (an instance of the directional similarity strategy), designed to capture the degree to which the contexts of a word, a, form a subset of the contexts of another word, b. The second approach (an instance of the relation classification strategy) represents a word pair, a : b, with a feature vector that is the concatenation of the context vectors of a and b, and then applies supervised learning to a training set of labeled feature vectors. Additionally, we introduce a third approach that is a new instance of the relation classification strategy. The third approach represents a word pair, a : b, with a feature vector in which the features are the differences in the similarities of a and b to a set of reference words. All three approaches use vector space models (VSMs) of semantics, based on word-context matrices. We perform an extensive evaluation of the three approaches using three different datasets. The proposed new approach (similarity differences) performs significantly better than the other two approaches on some datasets and there is no dataset for which it is significantly worse. Along the way, we address some of the concerns raised in past research, regarding the treatment of RLE as a problem of semantic relation classification, and we suggest it is beneficial to make connections between the research in lexical entailment and the research in semantic relation classification.

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“…They used a machine learning technique to combine features derived from both methods. The UNED-NLP Group Recognizing Textual Entailment System [13] was based on the use of a broad-coverage parser to extract dependency relations and a module which obtains lexical entailment relations from WordNet. The work aims at comparing whether the matching of dependency trees substructures give better evidence of entailment than the matching of plain text alone.…”

Section: Related Workmentioning

confidence: 99%