Improving Question Retrieval in Community Question Answering Using World Knowledge / 2239
Guangyou Zhou, Yang Liu, Fang Liu, Daojian Zeng, Jun Zhao
Community question answering (cQA), which provides a platform for people with diverse background to share information and knowledge, has become an increasingly popular research topic. In this paper, we focus on the task of question retrieval.The key problem of question retrieval is to measure the similarity between the queried questions and the historical questions which have been solved by other users. The traditional methods measure the similarity based on the bag-of-words(BOWs) representation. This representation neither captures dependencies between related words, nor handles synonyms or polysemous words. In this work, we first propose a way to build a concept thesaurus based on the semantic relations extracted from the world knowledge of Wikipedia. Then, we develop a unified framework to leverage these semantic relations in order to enhance the question similarity in the concept space. Experiments conducted on a real cQA data set show that with the help of Wikipedia thesaurus, the performance of question retrieval is improved as compared to the traditional methods.