Turkish factoid question answering using answer pattern matching

Abstract

Efficiently locating information on the Web has become one of the most important challenges in the last decade. The Web Search Engines have been used to locate the documents containing the required information. However, in many situations a user wants a particular piece of information rather than a document set. Question Answering (QA) systems have addressed this problem and they return explicit answers to questions rather than set of documents. Questions addressed by QA systems can be categorized into five categories: factoid, list, definition, complex, and speculative questions. A factoid question has exactly one correct answer, and the answer is mostly a named entity like person, date, or location. In this thesis, we develop a pattern matching approach for a Turkish Factoid QA system. In TREC-10 QA track, most of the question answering systems used sophisticated linguistic tools. However, the best performing system at the track used only an extensive list of surface patterns; therefore, we decided to investigate the potential of answer pattern matching approach for our Turkish Factoid QA system. We try different methods for answer pattern extraction such as stemming and named entity tagging. We also investigate query expansion by using answer patterns. Several experiments have been performed to evaluate the performance of the system. Compared with the results of the other factoid QA systems, our methods have achieved good results. The results of the experiments show that named entity tagging improves the performance of the system.