Paper: Learning to Read Between the Lines using Bayesian Logic Programs

ACL ID P12-1037
Title Learning to Read Between the Lines using Bayesian Logic Programs
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

Most information extraction (IE) systems identify facts that are explicitly stated in text. However, in natural language, some facts are implicit, and identifying them requires ?read- ing between the lines?. Human readers nat- urally use common sense knowledge to in- fer such implicit information from the explic- itly stated facts. We propose an approach that uses Bayesian Logic Programs (BLPs), a statistical relational model combining first- order logic and Bayesian networks, to infer additional implicit information from extracted facts. It involves learning uncertain common- sense knowledge (in the form of probabilis- tic first-order rules) from natural language text by mining a large corpus of automatically ex- tracted facts. These rules are then used to de- rive additional facts from e...