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Consumer Health Question Answering Systems Rohit Chandra 2011090 Sourabh Singh 2011112
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Plan Introduction Architecture Stages Question Processing Query formulation Document Retrieval Sentence Extraction Answer ranking Corpus Existing health QA system Questions
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Introduction A system which gives automated answers to consumer health queries Can answer questions like – What treatment to get ? Symptoms – Disease Prescribed medicines for an ailment Prognosis of a disease Many QA systems exist in the clinical domain Clinical QA systems take well formed queries in pre-defined templates Consumer health queries are ill formed and many times have lots of unnecessary detail
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Architecture
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Stages Receiving user query Question processing and analysis (NLP) Question classification for search in specific DBs Keyword extraction and query formulation Document retrieval (TF-IDF) Sentence extraction (SemRep) Ranking of candidate answers Displaying the answers
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Question processing Fixing grammatical errors, stemming and tokenization, POS tagging Stanford NLP tools Anaphora and Ellipsis resolution Useful in decomposition Divides the complex query into independent meaningful sentences Focus term determination Svm-light & weka Metamap for UMLS entities Question classification SVM Light, ClinQues, Stanford NLP Tools 12 categories viz. diagnosis, treatment, prognosis, medication etc.
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Question Decomposition
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Features Unigrams (UMLS entities) Semantic groupings UMLS semantic type Sentence offset Lexicon Offset POS tags Bigrams Parse tree tags
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Query expansion/Keywords UMLS Metathesaurus online https://uts.nlm.nih.gov///metathesaurus.html
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Document retrieval Tf-IDF using Apache Lucene – simple keyword based retrieval Apache Solr – searching of locally indexed documents OAQA – keyword search along with semantic and expansion information using UMLS and Wordnet Bing API
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Sentence extraction This is done using Metamap and SemRep Both tools provide Java and Web APIs Takes paragraphs with upto 10000 words Output in XML format
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Candidate answer ranking Keyword frequency Tf-IDF score Lesk Score Longest Common Subsequence
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Corpus Online and offline documents for fetching answers and for training data UMLS Metathesaurus MeSH Medline Plus ClinQues Yahoo Answers Steps Tools – Illness & prescription database
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Existing health QA systems askHERMES – UW Milwaukee QANUS – NUS MiPACQ – University of Colorado Boulder WATSON – IBM MD Consult – www.mdconsult.comwww.mdconsult.com Illnesses and prescription database
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References NIH health QA system http://lhncbc.nlm.nih.gov/project/consumer-health-information-and-question-answering http://lhncbc.nlm.nih.gov/project/consumer-health-information-and-question-answering Metamap, SemRep and UMLS metathesaurus http://skr3.nlm.nih.gov/ http://skr3.nlm.nih.gov/ Athenikos, Sofia J., and Hyoil Han. "Biomedical question answering: A survey."Computer methods and programs in biomedicine 99.1 (2010): 1-24. Cairns, Brian L., et al. "The MiPACQ clinical question answering system."AMIA Annual Symposium Proceedings. Vol. 2011. American Medical Informatics Association, 2011.
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Questions Thanks
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