A property related to words or the vocabulary of a language.
Published in Chapter:
A Lexico-Syntactic-Semantic Approach to Recognizing Textual Entailment
Rohini Basak (Department of Information Technology, Jadavpur University, India), Sudip Kumar Naskar (Computer Science and Engineering, Jadavpur University, India), and Alexander Gelbukh (Center for Computing Research, Instituto Politécnico Nacional, Mexico)
Copyright: © 2020
|Pages: 41
DOI: 10.4018/978-1-7998-3038-2.ch010
Abstract
Given two textual fragments, called a text and a hypothesis, respectively, recognizing textual entailment (RTE) is a task of automatically deciding whether the meaning of the second fragment (hypothesis) logically follows from the meaning of the first fragment (text). The chapter presents a method for RTE based on lexical similarity, dependency relations, and semantic similarity. In this method, called LSS-RTE, each of the two fragments is converted to a dependency graph, and the two obtained graph structures are compared using dependency triple matching rules, which have been compiled after a thorough and detailed analysis of various RTE development datasets. Experimental results show 60.5%, 64.4%, 62.8%, and 61.5% accuracy on the well-known RTE1, RTE2, RTE3, and RTE4 datasets, respectively, for the two-way classification task and 54.3% accuracy for three-way classification task on the RTE4 dataset.