Learning from Reading Syntactically Complex Biology Texts

Rutu Mulkar, Jerry R. Hobbs, Eduard Hovy

This paper concerns learning information by reading natural language texts. The major aim is to develop representations that are understandable by a reasoning engine and can be used to answer questions. We use abduction to map natural language sentences into concise and specific underlying theories. Techniques for automatically generating usable datarepresentations are discussed. New techniques are proposed to obtain semantically correct and precise logical representations from natural language, in particular in cases where its syntactic complexity results in fragmented logical forms.

Subjects: 13. Natural Language Processing; 5. Common Sense Reasoning

Submitted: Jan 25, 2007

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.