Learning Textual Entailment on a Distance Feature Space

Textual Entailment recognition is a very difficult task as it is one of the fundamental problems in any semantic theory of natural language. As in many other NLP tasks, Machine Learning may offer important tools to better understand the problem. In this paper, we will investigate the usefulness of Machine Learning algorithms to address an apparently simple and well defined classification problem: the recognition of Textual Entailment. Due to its specificity, we propose an original feature space, the distance feature space, where we model the distance between the elements of the candidate entailment pairs. The method has been tested on the data of the Recognizing Textual Entailment (RTE) Challenge.

• Type: Conference
• Year: 2005
• Where: MLCW
• Authors: Maria Teresa Pazienza, Marco Pennacchiotti, Fabio Massimo Zanzotto
• Keywords: Machine Learning, MLCW 2005, Textual Entailment, Textual Entailment Recognition