Please wait a minute...
Data Analysis and Knowledge Discovery
Current Issue | Archive | Adv Search |
Deep Learning Based Query Suggestion for Community-based Question and Answering
Ding Heng,Li Yingxuan
(School of Information Management, Central China Normal University, Wuhan 100871, China)
Export: BibTeX | EndNote (RIS)      

[Objective] We aim to develop query suggestion technology for improving user experience on community-based question and answering platform.

[Methods] We proposed a neural network model CNMNN to calculate the correlation of intent between query and natural language question.

[Results] Comparing with the best performances of four existing methods, our CNMNN model gets 45%, 38.7%, 33.4%, 34.8% and 52.9% improvements on relevance metrics of nDCG@5, nDCG@10, nDCG@20, MRR and MAP, and gains 31.5%, 23.6%, 25.5%, 38.1%, 36.9% and 30.7% improvements on diversity metrics of α-nDCG@5, α-nDCG@10, α-nDCG@20 and ERR-IA@5, ERR-IA@10 and ERR-IA@20.

[Limitations] Although α-nDCG@k and ERR-IA@k are reported, we have not purposed special methods for suggestion result diversification.

[Conclusions] CNMNN can not only calculate the semantic relevance between query and natural language question at phrase level, but also avoid the problem of feature signal compression caused by hierarchical convolution operation.

Key words query suggestion      deep learning      community-based question and answering      
ZTFLH:  TP393 G35  

Cite this article:

Ding Heng, Li Yingxuan. Deep Learning Based Query Suggestion for Community-based Question and Answering . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL:     OR

[1] Huang Lu,Zhou Enguo,Li Daifeng. Text Representation Learning Model Based on Attention Mechanism with Task-specific Information[J]. 数据分析与知识发现, 2020, 4(9): 111-122.
[2] Xu Chenfei, Ye Haiying, Bao Ping. Automatic Recognition of Produce Entities from Local Chronicles with Deep Learning[J]. 数据分析与知识发现, 2020, 4(8): 86-97.
[3] Zhao Yang, Zhang Zhixiong, Liu Huan, Ding Liangping. Classification of Chinese Medical Literature with BERT Model[J]. 数据分析与知识发现, 2020, 4(8): 41-49.
[4] Yu Chuanming, Wang Manyi, Lin Hongjun, Zhu Xingyu, Huang Tingting, An Lu. A Comparative Study of Word Representation Models Based on Deep Learning[J]. 数据分析与知识发现, 2020, 4(8): 28-40.
[5] Wang Xinyun,Wang Hao,Deng Sanhong,Zhang Baolong. Classification of Academic Papers for Periodical Selection[J]. 数据分析与知识发现, 2020, 4(7): 96-109.
[6] Jiao Qihang,Le Xiaoqiu. Generating Sentences of Contrast Relationship[J]. 数据分析与知识发现, 2020, 4(6): 43-50.
[7] Wang Mo,Cui Yunpeng,Chen Li,Li Huan. A Deep Learning-based Method of Argumentative Zoning for Research Articles[J]. 数据分析与知识发现, 2020, 4(6): 60-68.
[8] Deng Siyi,Le Xiaoqiu. Coreference Resolution Based on Dynamic Semantic Attention[J]. 数据分析与知识发现, 2020, 4(5): 46-53.
[9] Yu Chuanming,Yuan Sai,Zhu Xingyu,Lin Hongjun,Zhang Puliang,An Lu. Research on Deep Learning Based Topic Representation of Hot Events[J]. 数据分析与知识发现, 2020, 4(4): 1-14.
[10] Su Chuandong,Huang Xiaoxi,Wang Rongbo,Chen Zhiqun,Mao Junyu,Zhu Jiaying,Pan Yuhao. Identifying Chinese / English Metaphors with Word Embedding and Recurrent Neural Network[J]. 数据分析与知识发现, 2020, 4(4): 91-99.
[11] Liu Tong,Ni Weijian,Sun Yujian,Zeng Qingtian. Predicting Remaining Business Time with Deep Transfer Learning[J]. 数据分析与知识发现, 2020, 4(2/3): 134-142.
[12] Chuanming Yu,Haonan Li,Manyi Wang,Tingting Huang,Lu An. Knowledge Representation Based on Deep Learning:Network Perspective[J]. 数据分析与知识发现, 2020, 4(1): 63-75.
[13] Mengji Zhang,Wanyu Du,Nan Zheng. Predicting Stock Trends Based on News Events[J]. 数据分析与知识发现, 2019, 3(5): 11-18.
[14] Jingjing Pei,Xiaoqiu Le. Identifying Coordinate Text Blocks in Discourses[J]. 数据分析与知识发现, 2019, 3(5): 51-56.
[15] Zhixiong Zhang,Huan Liu,Liangping Ding,Pengmin Wu,Gaihong Yu. Identifying Moves of Research Abstracts with Deep Learning Methods[J]. 数据分析与知识发现, 2019, 3(12): 1-9.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938