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Recgnizing-Textual-Entailment-NLP

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It solves RTE (Recognizing textual entailment) problem using sentence extraction to cover semantic variation by extracting subject, predicate and object from each sentence.

Dataset

Dataset used is Third Pascal Recognizing Textual Entailment Challenge (RTE-3) dataset. It has approximately has 800 pairs text (T) and hypothesis (H) with labels as True or False showing whether T entails H or not.

Methodology

  • Preprocessing- Formation of parse tree using Stanford NLP library.  Sentence extraction- Used part of speech and parse tree for sentence extraction.
  • Part of sentence extraction- Subject, Predicate and Object- Used part of speech tag and syntactic parse tree.
  • Feature extraction and classifier- TF-IDF is used for word weighing and feature table is formed.
  • After feature extraction, any classification algorithm can be used to classify whether the text and hypothesis are entailed or not