Paper: Learning Question Classifiers

ACL ID C02-1150
Title Learning Question Classifiers
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2002

In order to respond correctly to a free form factual ques- tion given a large collection of texts, one needs to un- derstand the question to a level that allows determining some of the constraints the question imposes on a pos- sible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer. This paper presents a machine learning approach to question classification. We learn a hierarchical classi- fier that is guided by a layered semantic hierarchy of an- swer types, and eventually classifies questions into fine- grained classes. We show accurate results on a large col- lection of free-form questions used in TREC 10.