|
|
Improving Online Q&A Service with Deep Learning |
Ding Heng(),Li Yingxuan |
School of Information Management, Central China Normal University, Wuhan 430079, China |
|
|
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
|
|
Corresponding Authors:
Ding Heng
E-mail: me@gmail.com
|
[1] |
李亚楠, 王斌, 李锦涛. 搜索引擎查询推荐技术综述[J]. 中文信息学报, 2010,24(6):75-84.
|
[1] |
( Li Ya’nan, Wang Bin, Li Jintao. A Survey of Query Suggestion in Search Engine[J]. Journal of Chinese Information Processing, 2010,24(6):75-84.)
|
[2] |
孟玲玲. 基于WordNet的语义相似性度量及其在查询推荐中的应用研究[D]. 上海: 华东师范大学, 2014.
|
[2] |
( Meng Lingling. Research on Semantic Similarity Metric Based on WordNet and Its Application in Query Suggestion[D]. Shanghai: East China Normal University, 2014.)
|
[3] |
Yang J M, Cai R, Jing F, et al. Search-based Query Suggestion[C]//Proceedings of the 17th ACM Conference on Information and Knowledge Management. 2008: 1439-1440.
|
[4] |
季岚石. 基于搜索日志的查询推荐算法研究[D]. 长春: 吉林大学, 2013.
|
[4] |
( Ji Lanshi. The Query Recommendation Algorithm Research Based on the Search Logs[D]. Changchun: Jilin University, 2013.)
|
[5] |
Ding H, Balog K. Generating Synthetic Data for Neural Keyword-to-Question Models[C]//Proceedings of the 4th ACM SIGIR International Conference on the Theory of Information Retrieval. 2018: 51-58.
|
[6] |
Xu J X, Croft W B. Quary Expansion Using Local and Global Document Analysis[J]. ACM SIGIR Forum, 2017,51(2):168-175.
doi: 10.1145/3130348.3130364
|
[7] |
Garigliotti D, Balog K. Generating Query Suggestions to Support Task-based Search[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2017: 1153-1156.
|
[8] |
Cao H H, Jiang D X, Pei J, et al. Context-aware Query Suggestion by Mining Click-through and Session Data[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2008: 875-883.
|
[9] |
Mei Q Z, Zhou D Y, Church K. Query Suggestion Using Hitting Time[C]//Proceedings of the 17th ACM Conference on Information and Knowledge Management. ACM, 2008: 469-478.
|
[10] |
张伟男. 社区型问答中问句检索关键技术研究[D]. 哈尔滨:哈尔滨工业大学, 2014.
|
[10] |
( Zhang Weinan. Research on Key Techniques of Question Retrieval for Community Question Answering[D]. Harbin: Harbin Institute of Technology, 2014.)
|
[11] |
刘欣, 席耀一, 王波, 等. WordNet和词向量相结合的句子检索方法[J]. 信息工程大学学报, 2017,18(4):486-491.
|
[11] |
( Liu Xin, Xi Yaoyi, Wang Bo, et al. WordNet and Word Embedding Based Sentence Retrieval Method[J]. Journal of Information Engineering University, 2017,18(4):486-491.)
|
[12] |
Xue X B, Jeon J, Croft W B. Retrieval Models for Question and Answer Archives[C]//Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2008: 475-482.
|
[13] |
Zhou G, Cai L, Zhao J, et al. Phrase-based Translation Model for Question Retrieval in Community Question Answer Archives[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. 2011: 653-662.
|
[14] |
Ichikawa H, Hakoda K, Hashimoto T, et al. Efficient Sentence Retrieval Based on Syntactic Structure[C]//Proceedings of the COLING/ACL on Main Conference Poster Sessions. ACL, 2006: 399-406.
|
[15] |
Wang K, Ming Z Y, Chua T S, et al. A Syntactic Tree Matching Approach to Finding Similar Questions in Community-based QA Services[C]//Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2009: 187-194.
|
[16] |
Cai L, Zhou G Y, Liu K, et al. Learning the Latent Topics for Question Retrieval in Community QA[C]//Proceedings of the 5th International Joint Conference on Natural Language Processing. 2011: 273-281.
|
[17] |
Zhang K, Wu W, Wu H C, et al. Question Retrieval with High Quality Answers in Community Question Answering[C]//Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM, 2014: 371-380.
|
[18] |
Gao Y J, Chen L, Li R, et al. Mapping Queries to Questions: Towards Understanding Users’ Information Needs[C]//Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2013: 977-980.
|
[19] |
Wu H C, Wu W, Zhou M, et al. Improving Search Relevance for Short Queries in Community Question Answering[C]//Proceedings of the 7th ACM International Conference on Web Search and Data Mining. ACM, 2014: 43-52.
|
[20] |
Fan Y X, Pang L, Hou J P, et al. MatchZoo: A Toolkit for Deep Text Matching[OL]. arXiv Preprint, arXiv:1707.07270, 2017.
|
[21] |
Guo J F, Fan Y X, Ai Q Y, et al. A Deep Relevance Matching Model for Ad-hoc Retrieval[C]//Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2016: 55-64.
|
[22] |
Pang L, Lan Y Y, Guo J F, et al. Text Matching as Image Recognition[OL]. arXiv Preprint, arXiv: 1602.06359, 2016.
|
[23] |
Glorot X, Bordes A, Bengio Y. Deep Sparse Rectifier Neural Networks[J]. Journal of Machine Learning Research, 2011,15:315-323.
|
[24] |
Aghdam H H, Heravi E J. Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification[M]. Springer, 2017.
|
[25] |
Kalchbrenner N, Grefenstette E, Blunsom P, et al. A Convolutional Neural Network for Modelling Sentences[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. ACL, 2014: 655-665.
|
[26] |
Fleiss J L, Cohen J. The Equivalence of Weighted Kappa and the Intraclass Correlation Coefficient as Measures of Reliability[J]. Educational and Psychological Measurement, 1973,33(3):613-619.
doi: 10.1177/001316447303300309
|
[27] |
Burges C J C. From RankNet to LambdaRank to LambdaMART: An Overview[R/OL].[2010-08-02]. https://www.microsoft.com/en-us/research/uploads/prod/2016/02/MSR-TR-2010-82.pdf.
|
[28] |
Sanderson M. Test Collection Based Evaluation of Information Retrieval Systems[J]. Foundations and Trends in Information Retrieval, 2010,4(4):247-375.
doi: 10.1561/1500000009
|
[29] |
Clarke C L A, Kolla M, Cormack G V, et al. Novelty and Diversity in Information Retrieval Evaluation[C]//Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2008: 659-666.
|
[30] |
Chapelle O, Ji S H, Liao C Y, et al. Intent-based Diversification of Web Search Results: Metrics and Algorithms[J]. Information Retrieval, 2011,14(6):572-592.
doi: 10.1007/s10791-011-9167-7
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|