Question answering system using Q & A site corpus Query expansion and answer candidate evaluation

Question Answering (QA) is a task of answering natural language questions with adequate sentences. This paper proposes two methods to improve the performance of the QA system using a Q&A site corpus. The first method is for the relevant document retrieval module. We proposed modification of measure of mutual information for the query expansion; we calculate it between two words in each question and a word in its answer in the Q&A site corpus not to choose the words that are not suitable. The second method is for the candidate answer evaluation module. We proposed to evaluate candidate answers using the two measures together, i.e., the Web relevance score and the translation probability. The experiments were carried out using a Japanese Q&A site corpus. They revealed that the first proposed method was significantly better than the original method when their accuracies and MRR (Mean Reciprocal Rank) were compared and the second method was significantly better than the original methods when their MRR were compared.