Textual Entailment Using Lexical And Syntactic Similarity

Bandyopadhyay, Sivaji; Gelbukh, Alexander

doi:10.5121/ijaia.2011.2104

Cited by 19 publications

(19 citation statements)

References 14 publications

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“…1 In Ref. [21], the authors present the results of their competitive algorithm in the past five RTE challenges. In terms of F-measure, their top scores range from 0.529 (RTE-1) to 0.671 (RTE-3).…”

Section: Related Workmentioning

confidence: 99%

Cross-Sectional Relatedness Between Sentences in Breast Radiology Reports: Development of an SVM Classifier and Evaluation Against Annotations of Five Breast Radiologists

et al. 2013

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Introduce the notion of cross-sectional relatedness as an informational dependence relation between sentences in the conclusion section of a breast radiology report and sentences in the findings section of the same report. Assess inter-rater agreement of breast radiologists. Develop and evaluate a support vector machine (SVM) classifier for automatically detecting cross-sectional relatedness. A standard reference is manually created from 444 breast radiology reports by the first author. A subset of 37 reports is annotated by five breast radiologists. Inter-rater agreement is computed among their annotations and standard reference. Thirteen numerical features are developed to characterize pairs of sentences; the optimal feature set is sought through forward selection. Inter-rater agreement is F-measure 0.623. SVM classifier has F-measure of 0.699 in the 12-fold cross-validation protocol against standard reference. Report length does not correlate with the classifier's performance (correlation coefficient = -0.073). SVM classifier has average F-measure of 0.505 against annotations by breast radiologists. Mediocre inter-rater agreement is possibly caused by: (1) definition is insufficiently actionable, (2) fine-grained nature of cross-sectional relatedness on sentence level, instead of, for instance, on paragraph level, and (3) higher-than-average complexity of 37-report sample. SVM classifier performs better against standard reference than against breast radiologists's annotations. This is supportive of (3). SVM's performance on standard reference is satisfactory. Since optimal feature set is not breast specific, results may transfer to non-breast anatomies. Applications include a smart report viewing environment and data mining.

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Section: Related Workmentioning

confidence: 99%

Cross-Sectional Relatedness Between Sentences in Breast Radiology Reports: Development of an SVM Classifier and Evaluation Against Annotations of Five Breast Radiologists

et al. 2013

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“…Therefore, most of the proposed systems have been based on machine learning approaches which model the entailment relation over a variety of conventional features varying from basic lexical features to deep semantic features (Inkpen et al, ;Pakray et al, 2011;Zanzotto and Moschitti, 2006;Malakasiotis and Androutsopoulos, 2007). External semantic resources such as WordNet and VerbOcean have been extensively used to capture the semantic relationships between words in the H and T, and also to further enhance the entailment recognition system (Iftene and Moruz, 2009;Mehdad et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the Third Arabic Natural Language Processing Workshop

Habash¹,

Diab²,

Darwish³

et al. 2017

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A number of Arabic NLP (or Arabic NLP-related) workshops and conferences have taken place in the last few years, both in the Arab World and in association with international conferences. The Arabic NLP workshop at EACL 2017 follows in the footsteps of these previous efforts to provide a forum for researchers to share and discuss their ongoing work. This particular workshop is the third in a series, following the First Arabic NLP workshop held at EMNLP 2014 in Doha, Qatar; and the Second Arabic NLP workshop held at ACL 2015 in Beijing, China.We received 47 submissions and selected 22 (47% acceptance rate) for presentation in the workshop. All papers were reviewed by three reviewers on average. The number of submissions is over twice that of the previous workshop in Beijing, which also had a higher acceptance rate (65%). Ten papers will be presented orally and 12 as part of a poster session. The presentation mode is independent of the ranking of the papers. The papers cover a diverse set of topics from Maltese and Arabic dialect processing to models of semantic similarity and credibility analysis, advances in Arabic treebanking, and error annotation for dyslexic texts.The quantity and quality of the contributions to the workshop are strong indicators that there is a continued need for this kind of dedicated Arabic NLP workshop.We would like to acknowledge all the hard work of the submitting authors and thank the reviewers for the valuable feedback they provided. We hope these proceedings will serve as a valuable reference for researchers and practitioners in the field of Arabic NLP and NLP in general.Nizar Habash, General Chair, on behalf of the organizers of the workshop. AbstractThis paper presents a language identification system designed to detect the language of each word, in its context, in a multilingual documents as generated in social media by bilingual/multilingual communities, in our case speakers of Algerian Arabic. We frame the task as a sequence tagging problem and use supervised machine learning with standard methods like HMM and Ngram classification tagging. We also experiment with a lexicon-based method. Combining all the methods in a fall-back mechanism and introducing some linguistic rules, to deal with unseen tokens and ambiguous words, gives an overall accuracy of 93.14%. Finally, we introduced rules for language identification from sequences of recognised words.

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“…The entailment judgment is typically cast as a classification decision: true entailment if the relation holds and false otherwise. Therefore, most of the proposed systems have been based on machine learning approaches which model the entailment relation over a variety of conventional features varying from basic lexical features to deep semantic features (Inkpen et al, ;Pakray et al, 2011;Zanzotto and Moschitti, 2006;Malakasiotis and Androutsopoulos, 2007). External semantic resources such as WordNet and VerbOcean have been extensively used to capture the semantic relationships between words in the H and T, and also to further enhance the entailment recognition system (Iftene and Moruz, 2009;Mehdad et al, 2009).…”

Section: Related Workmentioning

confidence: 99%

Arabic Textual Entailment with Word Embeddings

AlMarwani¹,

Diab²

2017

Proceedings of the Third Arabic Natural Language Processing Workshop

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Determining the textual entailment between texts is important in many NLP tasks, such as summarization, question answering, and information extraction and retrieval. Various methods have been suggested based on external knowledge sources; however, such resources are not always available in all languages and their acquisition is typically laborious and very costly. Distributional word representations such as word embeddings learned over large corpora have been shown to capture syntactic and semantic word relationships. Such models have contributed to improving the performance of several NLP tasks. In this paper, we address the problem of textual entailment in Arabic. We employ both traditional features and distributional representations. Crucially, we do not depend on any external resources in the process. Our suggested approach yields state of the art performance on a standard data set, ArbTE, achieving an accuracy of 76.2 % compared to current state of the art of 69.3 %.

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Textual Entailment Using Lexical And Syntactic Similarity

Abstract: A two-way Textual Entailment (TE)

Cited by 19 publications

References 14 publications

Cross-Sectional Relatedness Between Sentences in Breast Radiology Reports: Development of an SVM Classifier and Evaluation Against Annotations of Five Breast Radiologists

Cross-Sectional Relatedness Between Sentences in Breast Radiology Reports: Development of an SVM Classifier and Evaluation Against Annotations of Five Breast Radiologists

Proceedings of the Third Arabic Natural Language Processing Workshop

Arabic Textual Entailment with Word Embeddings

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