A Probabilistic Answer Type Model.pdfVIP

A Probabilistic Answer Type Model Christopher Pinchak Department of Computing Science University of Alberta Edmonton, Alberta, Canada pinchak@cs.ualberta.ca Dekang Lin Google, Inc. 1600 Amphitheatre Parkway Mountain View, CA lindek@ Abstract 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. 1 Introduction Given a question, people are usually able to form an expectation about the type of the answer, even if they do not know the actual answer. An accu- rate expectation of the answer type makes it much easier to select the answer from a sentence that contains the query words. Consider the question “What is the capital of Norway?” We would ex- pect the answer to be a city and could filter out most of the words in the following sentence: The landed aristocracy was virtually crushed by Hakon V, who reigned from 1299 to 1319, and Oslo became the capital of Norway, re- placing Bergen as the principal city of the kingdom. The goal of answer typing is to determine whether a word’s semantic type is appropriate as an answer for a question. Many previous ap- proaches to answer typing, e.g., (Ittycheriah et al., 2001; Li and Roth, 2002; Krishnan et al., 2005), employ a predefined set of answer types and use supervised learning or manually constructed rules to classify a question according to expected an- swer type. A disadvantage of this approach is that there will always be questions whose answers do not belong to any of the predefined