A Graph-Based Textual Entailment Method Aware of Real-World Knowledge

摘要

In this paper we propose an unsupervised methodology to solve the textual entailment task, that extracts facts associated to pair of sentences. Those extracted facts are represented as a graph. Then, two graph-based representations of two sentences may be further compared in order to determine the type of textual entailment judgment that they hold. The comparison method is based on graph-based algorithms for finding sub-graphs structures inside another graph, but generalizing the concepts by means of a real world knowledge database. The performance of the approach presented in this paper has been evaluated using the data provided in the Task 1 of the SemEval 2014 competition, obtaining 79% accuracy.