1. Learning Parse-Free Event-Based Features for Textual Entailment Recognition

    We propose new parse-free event-based features to be used in conjunction with lexical, syntactic, and semantic features of texts and hypotheses for Machine Learning-based Recognizing Textual Entailment. Our new similarity features are extracted without using shallow semantic parsers, but still lexical and compositional semantics are not left out. Our experimental results demonstrate that these features can improve the effectiveness of the identification of entailment and no-entailment relationships. Content Type Book ChapterDOI 10.1007/978-3-642-17432-2_19Authors Bahadorreza Ofoghi, Centre for Informatics and Applied Optimization, University of Ballarat, P.O. Box 663, Ballarat, Victoria 3350, AustraliaJohn Yearwood, Centre for Informatics and Applied Optimization ...
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD
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