Paper: Automatic Term Ambiguity Detection

ACL ID P13-2140
Title Automatic Term Ambiguity Detection
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013

While the resolution of term ambiguity is important for information extraction (IE) systems, the cost of resolving each in- stance of an entity can be prohibitively expensive on large datasets. To combat this, this work looks at ambiguity detec- tion at the term, rather than the instance, level. By making a judgment about the general ambiguity of a term, a system is able to handle ambiguous and unambigu- ous cases differently, improving through- put and quality. To address the term ambiguity detection problem, we employ a model that combines data from lan- guage models, ontologies, and topic mod- eling. Results over a dataset of entities from four product domains show that the proposed approach achieves significantly above baseline F-measure of 0.96.