Paper: Inducing Fine-Grained Semantic Classes via Hierarchical and Collective Classification

ACL ID C10-1105
Title Inducing Fine-Grained Semantic Classes via Hierarchical and Collective Classification
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

Research in named entity recognition and mention detection has typically involved a fairly small number of semantic classes, which may not be adequate if seman- tic class information is intended to sup- port natural language applications. Moti- vated by this observation, we examine the under-studied problem of semantic sub- type induction, where the goal is to au- tomatically determine which of a set of 92 fine-grained semantic classes a noun phrase belongs to. We seek to improve the standard supervised approach to this prob- lem using two techniques: hierarchical classification and collective classification. Experimental results demonstrate the ef- fectiveness of these techniques, whether or not they are applied in isolation or in combination with the standard approach.