Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (10): 37-46    DOI: 10.11925/infotech.2096-3467.2019.1301
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Improving Online Q&A Service with Deep Learning
Ding Heng(),Li Yingxuan
School of Information Management, Central China Normal University, Wuhan 430079, China
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Abstract

[Objective] This paper develops a neural network model to improve the online questioning and answering services.[Methods] First, we retrieved and constructed our experimental dataset from Yahoo Answers and Yahoo! L6 platform. Then, we proposed a neural network model (CNMNN) based on semantic matching matrix,variable-size convolutional layer, and multiple layer perceptron. Finally, we compared the results our model with the MQ2QC、IBLM、DRMM and MatchPyramid methods. [Results] The proposed model was 45.0%, 38.7%, 33.4%, 34.8% and 52.9% higher than the best results on relevance metrics of nDCG@5, nDCG@10, nDCG@20, MRR and MAP. It also gained 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] We did not include new method to further diversify the results.[Conclusions] The new CNMNN model can effectively calculate the semantic relevance between queries and natural language questions at phrase level. It also avoids the issue of feature signal compression due to hierarchical convolution operation.

Received: 04 December 2019      Published: 09 November 2020
 ZTFLH: TP393
Corresponding Authors: Ding Heng     E-mail: me@gmail.com