HealthQA: A Chinese QA Summary System for Smart Health

Although online health expert QA services can provide high quality information for health consumers, there is no Chinese question answering system built on knowledge from existing expert answers, leading to duplicated efforts of medical experts and reduced efficiency. To address this issue, we develop a Chinese QA system for smart health (HealthQA), which provides timely, automatic and valuable QA service. Our HealthQA collects diabetes expert question answer data from three major QA websites in China. We develop a hierarchical clustering method to group similar questions and answers, an extended similarity evaluation algorithm for retrieving relevant answers and a ranking based summarization for representing the answer. ROUGE and manual tests show that our system significantly outperforms the search engine.

[1]  Wei Song,et al.  Multi-aspect query summarization by composite query , 2012, SIGIR '12.

[2]  Luo Si,et al.  Probabilistic models for answer-ranking in multilingual question-answering , 2010, TOIS.

[3]  Harris Wu,et al.  Probabilistic question answering on the Web , 2005, J. Assoc. Inf. Sci. Technol..

[4]  Hong Yu,et al.  AskHERMES: An online question answering system for complex clinical questions , 2011, J. Biomed. Informatics.

[5]  Ming Che Lee,et al.  A novel sentence similarity measure for semantic-based expert systems , 2011, Expert Syst. Appl..

[6]  Stan Matwin,et al.  A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization , 2001 .

[7]  Michael Kaisser,et al.  The QuALiM Question Answering Demo: Supplementing Answers with Paragraphs drawn from Wikipedia , 2008, ACL.

[8]  Zuhair Bandar,et al.  Sentence similarity based on semantic nets and corpus statistics , 2006, IEEE Transactions on Knowledge and Data Engineering.

[9]  Yong Yu,et al.  Understanding and Summarizing Answers in Community-Based Question Answering Services , 2008, COLING.

[10]  Zhiwei Huang,et al.  A Chinese Question Answering System Using Web Service on Restricted Domain , 2010, 2010 International Conference on Artificial Intelligence and Computational Intelligence.

[11]  Weiguo Fan,et al.  Beyond keywords: Automated question answering on the web , 2008, CACM.

[12]  Chin-Yew Lin,et al.  ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.

[13]  Jaime Carbonell,et al.  Multi-Document Summarization By Sentence Extraction , 2000 .

[14]  Namje Park,et al.  Mobile, Ubiquitous, and Intelligent Computing - MUSIC 2013, FTRA 4th International Conference on Mobile, Ubiquitous, and Intelligent Computing, September 4-6, 2013, Gwangju, Korea , 2014, MUSIC.

[15]  Rada Mihalcea,et al.  Semantic Relatedness Using Salient Semantic Analysis , 2011, AAAI.

[16]  Hong Yu,et al.  Beyond Information Retrieval - Medical Question Answering , 2006, AMIA.

[17]  Yihong Gong,et al.  Multi-Document Summarization using Sentence-based Topic Models , 2009, ACL.

[18]  Jade Goldstein-Stewart,et al.  Selecting Text Spans for Document Summaries: Heuristics and Metrics , 1999, AAAI/IAAI.

[19]  Rodney D. Nielsen,et al.  The MiPACQ clinical question answering system. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[20]  Hyoil Han,et al.  Biomedical question answering: A survey , 2010, Comput. Methods Programs Biomed..

[21]  Shuo Xu,et al.  XML-Based Document Retrieval in Chinese Diseases Question Answering System , 2013, MUSIC.

[22]  Jade Goldstein-Stewart,et al.  The use of MMR, diversity-based reranking for reordering documents and producing summaries , 1998, SIGIR '98.

[23]  Weiwei Guo,et al.  Weiwei: A Simple Unsupervised Latent Semantics based Approach for Sentence Similarity , 2012, SemEval@NAACL-HLT.