A word clustering approach for language model-based sentence retrieval in question answering systems

Abstract

In this paper we propose a term clustering approach to improve the performance of sentence retrieval in Question Answering (QA) systems. As the search in question answering is conducted over smaller segments of data than in a document retrieval task, the problems of data sparsity and exact matching become more critical. In this paper we propose Language Modeling (LM) techniques to overcome such problems and improve the sentence retrieval performance. Our proposed methods include building class-based models by term clustering, and then employing higher order n-grams with the new class-based model. We report our experiments on the TREC 2007 questions from QA track. The results show that the methods investigated here enhanced the mean average precision of sentence retrieval from 23.62% to 29.91%.