Modeling actions of PubMed users with n-gram language models

TitleModeling actions of PubMed users with n-gram language models
Publication TypeJournal Articles
Year of Publication2009
AuthorsJimmy Lin, Wilbur WJ
JournalInformation retrieval
Volume12
Issue4
Pagination487 - 503
Date Published2009///
Abstract

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®, the public gateway to the MEDLINE® 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.

DOI10.1007/s10791-008-9067-7