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Automatic Term Ambiguity Detection

Tyler Baldwin, Yunyao Li and Bogdan Alexe

The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013


While the resolution of term ambiguity is important for information extraction (IE) systems, the cost of resolving each instance of an entity can be prohibitively expensive on large datasets. To combat this, this work looks at ambiguity detection 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 unambiguous cases differently, improving throughput and quality. To address the term ambiguity detection problem, we employ a model that combines data from language models, ontologies, and topic modeling. Results over a dataset of entities from four product domains show that the proposed approach achieves significantly above baseline F-measure of 0.96.

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