Feature Analysis for Paraphrase Recognition and Textual Entailment

Authors: Andrea Segura-Olivares, Alejandro García, Hiram Calvo

Research in Computing Science, Vol. 70, pp. 119-144, 2013.

Abstract: Paraphrase recognition is the task of Natural Language Processing of detecting if an expression restated as another expression contains the same information. Textual Entailment recognition, while being similar to paraphrase recognition, is a task that consists in finding out if a given text can be observed as a consequence of another text fragment, sometimes considering only part of the original meaning, or adding some inferences based on common sense. Traditionally, for solving this problem, several lexical, syntactic and semantic based techniques are used. In this work, we seek to use the less resources as possible, while being effective. For this, we perform a feature analysis for performing Paraphrase Recognition and recognizing Textual Entailment experimenting with the combination of several Natural Language Processing techniques like word overlapping, syntactic analysis, and elimination of stop words. Particularly, we explore using the syntactic n-grams technique combined with some auxiliary approaches such as stemming, synonym detection, similarity measures and linear interpolation. We measure and compare the performance of our system by using the Microsoft Research Paraphrase Corpus, and the RTE-3 test set for Paraphrasing and Textual Entailment, respectively. Syntactic n-grams produce good results for Paraphrase Recognition. As far as we know, syntactic n-grams had not been used for this task. For Textual Entailment, our best results were obtained by using a simple word overlapping algorithm based on stemming and elimination of stop words.

Keywords: Machine Learning, Paraphrase Recognition, Textual Entailment

PDF: Feature Analysis for Paraphrase Recognition and Textual Entailment
PDF: Feature Analysis for Paraphrase Recognition and Textual Entailment