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Relevant question answering in community based networks using deep LSTM neural networks

Karimi, E ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1109/CFIS.2019.8692168
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. Community based Question Answering (CQA) websites enable users to post their questions and their questions will be answered by other users. These group of social networking websites are one of the most popular websites on the Internet. The responses on these CQA websites can be for specific questions related to a specific field of interest to the users or to all kind of questions. Creating automated CQA websites is of great interest for the natural language processing research. One of task in development of automated CQA websites is finding similar questions to the question asked by the user. In this paper, a novel method for finding questions relevant questions to the question of a user using deep LSTM neural networks is proposed. Experimental results show that the proposed algorithm has high accuracy for finding questions in CQA social networks. © 2019 IEEE
  6. Keywords:
  7. Community based question answering ; LSTM ; Social network ; Deep learning ; Deep neural networks ; Intelligent systems ; Natural language processing systems ; Recurrent neural networks ; Social networking (online) ; Community-based ; Community-based question answering ; High-accuracy ; NAtural language processing ; Question Answering ; Long short-term memory
  8. Source: 7th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2019, 29 January 2019 through 31 January 2019 ; 2019 ; 9781728106731 (ISBN)
  9. URL: https://ieeexplore.ieee.org/abstract/document/8692168