Relevant Question Answering in Community Based Networks Using Deep LSTM Neural Networks

被引：4
|
作者：
Karimi, Elaheh [1 ]
Majidi, Babak [1 ]
Manzuri, Mohammad Taghi [2 ]
机构：
[1] Khatam Univ, Dept Comp Engn, Tehran, Iran
[2] Sharif Univ Technol, Dept Comp Engn, Tehran, Iran
来源：
2019 7TH IRANIAN JOINT CONGRESS ON FUZZY AND INTELLIGENT SYSTEMS (CFIS) | 2019年
关键词：
Social network; Community based question answering; recurrent neural networks; LSTM; deep learning;
D O I：
10.1109/cfis.2019.8692168
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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
引用
收藏
页码：36 / 40
页数：5
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