Genomic information retrieval through selective extraction and tagging by the ASU-BioAI group

Lian Yu, Syed Toufeeq Ahmed, Graciela Gonzalez, Brandon Logsdon, Mutsumi Nakamura, Shawn Nikkila, Kalpesh Shah, Luis Tari, Ryan Wendt, Amanda Zeigler, Chitta Baral

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

Abstract

In this paper we describe the approach used by the Arizona State University BioAI group for the ad-hoc retrieval task of the TREC Genomics Track 2005. We pre-process TREC query expression by adding the synonyms of genes, diseases, bio-processes, functions of organs, and selectively adding stemming verbs, nouns, and Mesh Heading categories. The pre-processed queries are used to perform initial search on the TREC Genomics collection of MEDLINE abstracts and produce a set of target abstracts using Apache Lucene. Tagging, anaphor resolution and fact extraction are performed on the target abstracts to refine the search results in terms of relevance. Finally, we rank the target abstracts according to the extracted facts, distance between terms and terms appeared in the query.

Original languageEnglish (US)
Title of host publicationNIST Special Publication
StatePublished - 2005
Event14th Text REtrieval Conference, TREC 2005 - Gaithersburg, MD, United States
Duration: Nov 15 2005Nov 18 2005

Other

Other14th Text REtrieval Conference, TREC 2005
CountryUnited States
CityGaithersburg, MD
Period11/15/0511/18/05

ASJC Scopus subject areas

  • Engineering(all)

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    Yu, L., Ahmed, S. T., Gonzalez, G., Logsdon, B., Nakamura, M., Nikkila, S., Shah, K., Tari, L., Wendt, R., Zeigler, A., & Baral, C. (2005). Genomic information retrieval through selective extraction and tagging by the ASU-BioAI group. In NIST Special Publication