Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
All questions are implicitly associated with an expected answer type. Unlike previous approaches that require a predefined 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 potential answer by the probability that it fits into the question contexts. Evaluation is performed against manual and semiautomatic methods using a fixed set of answer labels. | 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@google.com Abstract All questions are implicitly associated with an expected answer type. Unlike previous approaches that require a predefined 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 potential answer by the probability that it fits into the question contexts. Evaluation is performed against manual and semiautomatic methods using a fixed set of answer 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 accurate 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 expect 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 replacing 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 approaches 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 answer type. A disadvantage of this approach is that there will always be questions whose answers do not belong to any of the predefined types. Consider the question .
