Semantic inference at the lexical-syntactic level

Semantic inference is an important component in many natural language understanding applications. Classical approaches to semantic inference rely on complex logical representations. However, practical applications usually adopt shallower lexical or lexical-syntactic representations, but lack a principled inference framework. We propose a generic semantic inference framework that operates directly on syntactic trees. New trees are inferred by applying entailment rules, which provide a unified representation for varying types of inferences. Rules were generated by manual and automatic methods, covering generic linguistic structures as well as specific lexical-based inferences. Initial empirical evaluation in a Relation Extraction setting supports the validity of our approach. Copyright © 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.