Recognition of Partial Textual Entailment for Indian Social Media Text

Dwijen Rudrapal, Amitava Das, Baby Bhattacharya


Textual entailment (TE) is a unidirectional relationship between two expressions where the meaning of one expression called Hypothesis (H), infers from the other expression called Text (T). The definition of TE is rigid in a sense that if the H entails from T but lacks minor information or have some additional
information, then the pair treated as non-entailed. In such cases we could not measure the relatedness of a T-H pair. Partial textual entailment (PTE) is a possible solution of this problem which defines partial entailment relation between a T-H pair. PTE relationship can plays an important role in different Natural Language Processing (NLP) applications like text summarization and question-answering system by reducing redundant information. In this paper we investigate the idea of PTE for Indian social media text (SMT). We developed a PTE annotated corpus for Bengali tweets and proposed a Sequential Minimal Optimization (SMO) based PTE recognition approach. We also evaluated our proposed approach through experiment results.


Textual entailment, social media text, text summarization, partial textual entailment, question-answering system, machine learning

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