Recognizing Textual Entailment Using Inductive Logic Programming

Textual entailment is the problem of recognizing, given two pieces of text, if the meaning of one piece of text can be inferred from (is entailed by) another. Recognizing textual entailment (RTE) is an important task in natural language processing (NLP) systems and many tools are available that aid the process of extraction of characteristics from text. Inductive logic programming algorithms (ILP) are machine learning algorithms that use labeled examples and an encoding of background knowledge to infer hypotheses that entail the examples. In this project, we use inductive logic programming algorithms to solve RTE tasks. We extract and process data characteristics by using relations from text and evaluate these relations along with the effectiveness of ILP in solving RTE tasks.unpublishednot peer reviewedU of I OnlyUndergraduate senior thesis not recommended for open acces