1. A Statistics-Based Semantic Textual Entailment System

    We present a Textual Entailment (TE) recognition system that uses semantic features based on the Universal Networking Language (UNL). The proposed TE system compares the UNL relations in both the text and the hypothesis to arrive at the two-way entailment decision. The system has been separately trained on each development corpus released as part of the Recognizing Textual Entailment (RTE) competitions RTE-1, RTE-2, RTE-3 and RTE-5 and tested on the respective RTE test sets. Content Type Book ChapterPages 267-276DOI 10.1007/978-3-642-25324-9_23Authors Partha Pakray, Computer Science and Engineering Department, Jadavpur University, Kolkata, IndiaUtsab Barman, Computer Science and Engineering Department ...
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD
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