Language Model Combination for Community-based Q & A Retrieval

This paper proposes three methods for combining various probabilistic models for retrieving answers from community-based question answering (cQA) archives. We adopt four probabilistic models for these combinations, i.e., (1) the language model measuring similarity between a query and a question stored in the cQA archive, (2) two translation models for measuring the similarity between a query and an answer stored in the cQA archive, and a background language model for smoothing. Then, we developed three parameter estimation methods. Two of them are mixture models of the language models. The remaining model exploits the difference between the models. We apply the proposed methods to a cQA archive and show that they significantly outperform a widely used language model and Okapi BM25. We also show that they achieve a better performance than the recently proposed cQA retrieval method.