Information Retrieval On Empty Fields

Victor Lavrenko, Xing Yi, James Allan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We explore the problem of retrieving semi-structured documents from a real-world collection using a structured query. We formally develop Structured Relevance Models (SRM), a retrieval model that is based on the idea that plausible values for a given field could be inferred from the context provided by the other fields in the record. We then carry out a set of experiments using a snapshot of the National Science Digital Library (NSDL) repository, and queries that only mention fields missing from the test data. For such queries, typical field matching would retrieve no documents at all. In contrast, the SRM approach achieves a mean average precision of over twenty percent.
Original languageEnglish
Title of host publicationHuman Language Technology 2007
Subtitle of host publicationThe Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference April 22-27, 2007, Rochester, New York, USA
Number of pages8
Publication statusPublished - Apr 2007


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