More Web Proxy on the site http://driver.im/

research-article

An answer recommendation algorithm for medical community question answering systems

Authors:

Feiyue WangAuthors Info & Claims

2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

Pages 139 - 144

https://doi.org/10.1109/SOLI.2016.7551676

Published: 01 July 2016 Publication History

Abstract

Online medical community question answering (cQA) systems are playing important roles as a supplement of the traditional medical service systems. The answer recommendation service provides guidance to diagnose diseases and brings convenience and valuable reference to users. In this paper, we propose a method to recommend answer for particular questions on medical cQA system. There are three steps for the answer recommendation system, including similar cases retrieval, answers quality estimation and answer recommendation. The proposed algorithm is tested on a dataset collected from online medical community question answering system. The results show a good performance in answer recommendation.

References

[1]

G. Salton, A. Wong and C.-S. Yang, “A vector space model for automatic indexing,” Communications of the ACM, vol. 18, pp. 613–620, 1975.

[2]

A. Singhal, G. Salton, M. Mitra and C. Buckley, “Document length normalization,” Information Processing & Management, vol. 32, pp. 619–633, 1996.

Digital Library

[3]

C. Zhai and J. Lafferty, “A study of smoothing methods for language models applied to ad hoc information retrieval,” in Proceedings of the th annual international ACM SIGIR conference on Research and development in information retrieval, 2001, pp. 334–342.

[4]

A. Berger, R. Caruana, D. Cohn, D. Freitag and V. Mittal, “Bridging the lexical chasm: statistical approaches to answer-finding,” in Proceedings of the 23rd annual internationalACM SIGIR conference on Research and development in information retrieval, 2000, pp. 192–199.

[5]

A. Berger and J. Lafferty, “Information retrieval as statistical translation,” in Proceedings of the 22nd annual internationalACM SIGIR conference on Research and development in information retrieval, 1999, pp. 222–229.

[6]

J. Jeon, W. B. Croft and J. H. Lee, “Finding semantically similar questions based on their answers,” in Proceedings of the 28th annual internationalACM SIGIR conference on Research and development in information retrieval, 2005, pp. 617–618.

[7]

J. Jeon, W. B. Croft and J. H. Lee, “Finding similar questions in large question and answer archives,” in Proceedings of the 14th ACM internationalconference on Information and knowledge management, 2005, pp. 84–90.

[8]

X. Xue, J. Jeon and W. B. Croft, “Retrieval models for question and answer archives,” in Proceedings of the 31st annual internationalACM SIGIR conference on Research and development in information retrieval, 2008, pp. 475–482.

[9]

G. Zhou, L. Cai, J. Zhao and K. Liu. Phrase-based translation model for questionretrieval in community question answer archives. In Annual Meeting of the.Associationfor Computational Linguistics (ACL 2011), pages 653 – 662, 2011.

[10]

Cai, Li et al., “Learning the Latent Topics for Question Retrieval in Community QA.” International Joint Conference on Natural Language Processing 2015.

[11]

Ji, Zongcheng et al., “Question-answer topic model for question retrieval in community question answering.” ACM International Conference on Information and Knowledge Management, pp. 2471–2474, 2012.

[12]

Zhou, Guangyou et al., “Learning Continuous Word Embedding with Metadata for Question Retrieval in Community Question Answering.” Meeting of the Association for Computational Linguistics and the, International Joint Conference on Natural Language Processing, pp.:281–282, 2015.

[13]

R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multi task learning,” in Proceedings of the 25th internationalconference on Machine learning, 2008, pp. 160–167.

[14]

A. Mnih and G. E. Hinton, “A scalable hierarchical distributed language model,” in Advances in neural information processing systems, 2009, pp. 1081–1088.

[15]

Turian, Joseph, L. Ratinov and Y. Bengio. “Word representations: A simple and general method for semisupervised learning.” In ACL 2010:384-394.

[16]

Harper F. Maxwell et al., “Predictors of answer quality in online Q&A sites.” Sigchi Conference on Human Factors in Computing Systems ACM, 2008:865–874.

[17]

Shah, Chirag and J. Pomerantz. “Evaluating and predicting answer quality in community QA.” Proceeding of the, International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, Geneva, Switzerland, July 2010:411–418.

[18]

Jeon, Jiwoon et al., “A framework to predict the quality of answers with non-textual features.” International ACM SIGIR Conference on Research and Development in Information Retrieval ACM, 2006:228-235.

[19]

Lee, Jung Tae, Y. I. Song and H. C. Rim. “Predicting the Quality of Answers Using Surface Linguistic Features.” International Conference on Advanced Language Processing and Web Information Technology 2007:111–116.

[20]

Agichtein, Eugene et al., Finding high-quality content in social media.Proceedings of the International Conference on Web Search and Web Data Mining, WSDM, Palo Alto, California, USA, February 2008 11-12 2008 2008:183–194.

[21]

Jurczyk, Pawel and E. Agichtein. “Discovering authorities in question answer communities by using link analysis.” Proceedings of the sixteenth ACM conference on Conference on informatioz and knowledge management ACM, 2007:919–922.

[22]

Sun, Ke et al., Learning to recommend questions based on user ratings. Acm Conference on Information & Knowledge Management 2009:751–758.

[23]

X. Cao, G. Cong, B. Cui, C. S. Jensen and C. Zhang, “The use of categorization information in language models for question retrieval,” in Proceedings of the 18th ACM conference on Information and knowledge management, 2009, pp. 265–274.

[24]

X. Cao, G. Cong, B. Cui and C. S. Jensen, “A generalized framework of exploring category information for question retrieval in community question answer archives,” in Proceedings of the 19th internationalconference on World wide web, 2010, pp. 201–210.

[25]

H. Duan, Y. Cao, C.-Y. Lin and Y. Yu, “Searching Questions by Identifying Question Topic and Question Focus,” in ACL, 2008, pp. 156–164.

[26]

Collobert R, Weston J, Bottou L, Natural language processing (almost) from scratch The Journal of Machine Learning Research, 2011, 12: 2493–2537.

Digital Library

[27]

Socher, Richard et al., “Semi-supervised recursive autoencoders for predicting sentiment distributions.” Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, 27–31 July 2011, John Mcintyre Conference Centre, Edinburgh, Uk, A Meeting of Sigdat, A Special Interest Group of the ACL 2011: 151-161.

[28]

Iyyer, Mohit et al., “Political Ideology Detection Using Recursive Neural Networks.” Meeting of the Association for Computational Linguistics 2014:1113–1122.

[29]

Kim, Yoon and Y. Kim. “Convolutional Neural Networks for Sentence Classification.” Eprint Arxiv (2014).

[30]

Mikolov T, Chen K, Corrado G et al., “Efficient estimation of word representations in vector space,” arXiv preprint arXiv:1301.3781, 2013.

[31]

Lee, J.-T. Kim, S.-B. Song, Y.-I. and H.-C. Rim, “Bridging lexical gaps between queries and questions on large online Q&A collections with compact translation models,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2008, pp. 410–418.

Cited By

Cui LLee DAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)KETCHProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532008(492-501)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532008

Index Terms

An answer recommendation algorithm for medical community question answering systems
1. Applied computing
  1. Life and medical sciences
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

Recommendations

User correlation model for question recommendation in community question answering
Abstract
In this paper, we address the problem of question recommendation that automatically recommends a new question to suitable users to answer in community question answering (CQA). The major challenge of question recommendation is the accurate ...
Question Recommendation in Medical Community-Based Question Answering
Neural Information Processing
Abstract
The medical community question answering system (MCQA) which is a new kind of medical information exchange platform is becoming more and more popular. Due to the number of patients is much more than the doctors, resulting in many patients can not ...
A community question-answering refinement system
HT '11: Proceedings of the 22nd ACM conference on Hypertext and hypermedia

Community Question Answering (CQA) websites, which archive millions of questions and answers created by CQA users to provide a rich resource of information that is missing at web search engines and QA websites, have become increasingly popular. Web users ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

266 pages

Copyright © 2016.

Publisher

IEEE Press

Publication History

Published: 01 July 2016

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Cui LLee DAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)KETCHProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532008(492-501)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532008

View Options

View options

Media

Figures

Other

Tables

View Table of Contents