| Modeling Actions of PubMed Users with N-Gram Language Models. | |
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MedLine Citation:
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PMID: 19684883 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Transaction logs from online search engines are valuable for two reasons: First, they provide insight into human information-seeking behavior. Second, log data can be used to train user models, which can then be applied to improve retrieval systems. This article presents a study of logs from PubMed((R)), the public gateway to the MEDLINE((R)) database of bibliographic records from the medical and biomedical primary literature. Unlike most previous studies on general Web search, our work examines user activities with a highly-specialized search engine. We encode user actions as string sequences and model these sequences using n-gram language models. The models are evaluated in terms of perplexity and in a sequence prediction task. They help us better understand how PubMed users search for information and provide an enabler for improving users' search experience. |
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Authors:
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Jimmy Lin; W John Wilbur |
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Publication Detail:
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Type: JOURNAL ARTICLE |
Journal Detail:
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Title: Information retrieval Volume: 12 ISSN: - ISO Abbreviation: Inf Retr Boston Publication Date: 2008 Sep |
Date Detail:
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Created Date: 2009-8-17 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 100963218 Medline TA: Inf Retr Boston Country: - |
Other Details:
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Languages: ENG Pagination: 487-503 Citation Subset: - |
Affiliation:
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The iSchool, College of Information Studies, University of Maryland, College Park, Maryland, USA, jimmylin@umd.edu. |
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Descriptor/Qualifier:
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| Grant Support | |
ID/Acronym/Agency:
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NIH0012203604//PHS HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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