Question Retrieval in Community Question Answering Enhanced by Tags Information in a Deep Neural Network Framework

Community Question Answering (CQA) platforms need to be easy and fast in question or answer exploration. It is common to use tags to categorize items in these platforms, and create taxonomies that assist exploration, indexing and searching. The focus of this thesis lies in recommending similar questions (Question Retrieval) by simultaneously deciding whether the contexts of two questions are similar, and which tags are applicable for each question. Current methods targeted for Question Retrieval in CQA either consider deep learning approaches (Lei et. al. 2016, Bogdanova et al. 2015), or conventional approaches that utilize the available information on questions' tags (Cao et. al. 2010, Zhou et al. 2013). The former framework is proved to be more powerful –especially in the case with loads of available data–, while the later is faster and successful in cases with few data. In this thesis, a deep learning approach for both question retrieval and tags recommendation is proposed, and their joint learning is found successful for transferring knowledge in the Question Retrieval task, after applying it on the AskUbuntu forum data. Additionally the neural network based Tag Recommendation performs better than the existing conventional methods. References: Dasha Bogdanova, Cı̕cero Nogueira dos Santos, Luciano Barbosa, and Bianca Zadrozny. Detecting se-mantically equivalent questions in online user forums. In CoNLL, 2015. Xin Cao, Gao Cong, Bin Cui, and Christian S. Jensen. A generalized framework of exploring category information for question retrieval in community question answer archives. In Proceedings of the 19th International Conference on World Wide Web, pages 201–210, New York, NY, USA, 2010. ACM. Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi S. Jaakkola, Kateryna Tymoshenko, Alessandro Mos-chitti, and Lluı̕s Mà rquez i Villodre. Semi-supervised question retrieval with gated convolutions. In HLT-NAACL, 2016. Guangyou Zhou, Yubo Chen, Daojian Zeng, and Jun Zhao. Towards faster and better retrieval models for question search. In Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management, CIKM 2013, pages 2139–2148, New York, NY, USA, 2013. ACM.