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Data Analysis and Knowledge Discovery
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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)
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Abstract  

[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:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.1301     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y0/V/I/1

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