A Knowledge Based Approach for Recognizing Textual Entailment for Natural Language Inference using Data Mining

摘要

Recognizing Textual Entailment (RTE) is a relatively new problem necessary for Natural Language Understanding (NLU) and Automated Knowledge Discovery. Natural Language Inference has dealt with approaches like the bag of words approach, formal methods (First order Logic) and pattern relation extraction which usually do not show satisfactory results. In this paper, Knowledge based approach has been proposed, utilizing data mining concepts on large text which is appropriately classified. Different Lexical resources like WordNet, VerbNet, ConceptNet have been integrated into a rich knowledge base, to provide semantics and structural information on English words. The data mined is used by an Inference system to give the output to the problem. The complete concept presented in the paper has been implemented in the form of a movie search engine wherein the knowledge based RTE concept has been employed on ?summaries or plots of the movies? internally to get best possible classification of the movies. The experiments have shown encouraging results, reduced the time of search and provided more accurate results. To the best of our knowledge, it is the first time that RTE concept has been implemented to Information Search in the form of Movie Search Engine.