Paper: A Probabilistic Answer Type Model

ACL ID E06-1050
Title A Probabilistic Answer Type Model
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

All questions are implicitly associated with an expected answer type. Unlike previous approaches that require a prede- fined set of question types, we present a method for dynamically constructing a probability-based answer type model for each different question. Our model evaluates the appropriateness of a poten- tial answer by the probability that it fits into the question contexts. Evaluation is performed against manual and semi- automatic methods using a fixed set of an- swer labels. Results show our approach to be superior for those questions classified as having a miscellaneous answer type.