Web-Based Unsupervised Learning to Query Formulation for Question Answering
Jason S. Chang
|關鍵字:||問答系統;問句類型分析;查詢詞擴充;question answering;question type extraction;query expansion|
This thesis investigates ways of learning how to formulate and expand a query to find the answer on the Web for a given natural language question. In our approach, the question pattern extracted from a given question is transformed into a set of query terms to improve the performance of an underlying search engine. In the training phase, the method involves crawling the Web for passages relevant to many pairs of question and answer, extracting of question patterns for fine-grained answer classification based on linguistic and statistical information, and aligning question patterns and keywords with n-grams in the answer passages. At runtime, any given question is converted into a question pattern which is then transformed to their top-ranking alignment counterparts as a way of formulating an expanded query so as to increase the possibilities of retrieve passages containing the answers. We also describe Atlas (Automatic Transform Learning by Aligning Sentences of question and answer), a prototype implementation of the proposed method. Independent evaluation on a set of questions shows that Atlas performs better than a naive keyword-based approach. This method also obviously reduces the human effort of seeking answers, since our system has higher recall rates when a handful of summaries are examined. Our straightforward method improves the most critical stage in question answering systems and also sheds new light on the long-standing problems of query expansion and relevance feedback.
|Appears in Collections:||Thesis|
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