Ensemble similarity measure for community-based question answer

Abstract

Community-based question answer (CQA) makes a figure network in development of social network. Similar question retrieval is one of the most important tasks in CQA. Most of the previous works on similar question retrieval were given with the underlying assumption that answers are similar if their questions are similar, but no work was done by modeling similarity measure with the constraint of the assumption. A new method of modeling similarity measure is proposed by constraining the measure with the assumption, and employing ensemble learning to get a comprehensive measure which integrates different context features for similarity measuring, including lexical, syntactic, semantic and latent semantic. Experiments indicate that the integrated model could get a relatively high performance consistence between question set and answer set. Models with better consistency tend to get a better precision according to answers.