Learning Continuous Word Embedding with Metadata for Question Retrieval in Community Question Answering

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages 250–259, Beijing, China, July 26-31, 2015. c⃝2015 Association for Computational Linguistics Learning Continuous Word Embedding with Metadata for Question Retrieval in Community Question Answering Guangyou Zhou1, Tingting He1, Jun Zhao2, and Po Hu1 1 School of Computer, Central China Normal University, Wuhan 430079, China 2 National Laboratory of Pattern Recognition, CASIA, Beijing 100190, China {gyzhou,tthe,phu}@mail.ccnu.edu.cn jzhao@nlpr.ia.ac.cn Abstract Community question answering (cQA) has become an important issue due to the popularity of cQA archives on the web. This paper is concerned with the problem of question retrieval. Question retrieval in cQA archives aims to find the exist- ing questions that are semantically equiv- alent or relevant to the queried questions. However, the lexical gap problem brings about new challenge for question retrieval in cQA. In this paper, we propose to learn continuous word embeddings with meta- data of category information within cQA pages for question retrieval. To deal with the variable size of word embedding vec- tors, we employ the framework of fisher kernel to aggregated them into the fixed- length vectors. Experimental results on large-scale real world cQA data set show that our approach can significantly out- perform state-of-the-art translation models and topic-based models for question re- trieval in cQA. 1 Introduction Over the past few years, a large amount of user- generated content have become an important in- formation resource on the web. These include the traditional Frequently Asked Questions (FAQ) archives and the emerging community question answering (cQA) services, such as Yahoo! An- swers1, Live QnA2, and Baidu Zhidao3. The con- tent in these web sites is usually organized as ques- tions and lists of answers associated with meta- data like user chosen categories to questions and askers’ awards to the best answers. This data made 1http://answers.yahoo.com/ 2http://qna.live.com/ 3http://zhidao.baidu.com/ cQA archives valuable resources for various tasks like question-answering (Jeon et al., 2005; Xue et al., 2008) and knowledge mining (Adamic et al., 2008), etc. One fundamental task for reusing content in cQA is finding similar questions for queried ques- tions, as questions are the keys to accessing the knowledge in cQA. Then the best answers of these similar questions will be used to answer the queried questions. Many studies have been done along this line (Jeon et al., 2005; Xue et al., 2008; Duan et al., 2008; Lee et al., 2008; Bernhard and Gurevych, 2009; Cao et al., 2010; Zhou et al., 2011; Singh, 2012; Zhang et al., 2014a). One big challenge for question retrieval in cQA is the lexi- cal gap between the queried questions and the ex- isting questions in the archives. Lexical gap means that the queried questions may contain words that are different from, but related to, the words in the existing questions. For example shown in (Zhang et al., 2014a), we find that for a queried question “how do I get knots out of my cats fur?”, there are good …

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