NTU at TREC 2006 genomics track

Kevin Hsin Yih Lin; Wen Juan Hou; Hsin Hsi Chen

NTU at TREC 2006 genomics track

Kevin Hsin Yih Lin, Wen Juan Hou, Hsin Hsi Chen

Department of Computer science & Information Engineering

Research output: Contribution to journal › Conference article

Abstract

In this paper, we present a system for information retrieval of biomedical texts at passage level. Our system used KL-divergence as the underlying retrieval model. We further added query expansion and performed post-processing on the results. We were able to obtain a Document MAP of 0.3563, Passage MAP of 0.0464 and Aspect MAP of 0.2255 on one of the three runs.

Original language	English
Journal	NIST Special Publication
Publication status	Published - 2006 Dec 1
Event	15th Text REtrieval Conference, TREC 2006 - Gaithersburg, MD, United States Duration: 2006 Nov 14 → 2006 Nov 17

ASJC Scopus subject areas

Engineering(all)

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Lin, K. H. Y., Hou, W. J., & Chen, H. H. (2006). NTU at TREC 2006 genomics track. NIST Special Publication.

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abstract = "In this paper, we present a system for information retrieval of biomedical texts at passage level. Our system used KL-divergence as the underlying retrieval model. We further added query expansion and performed post-processing on the results. We were able to obtain a Document MAP of 0.3563, Passage MAP of 0.0464 and Aspect MAP of 0.2255 on one of the three runs.",

author = "Lin, {Kevin Hsin Yih} and Hou, {Wen Juan} and Chen, {Hsin Hsi}",

year = "2006",

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AU - Lin, Kevin Hsin Yih

AU - Hou, Wen Juan

AU - Chen, Hsin Hsi

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N2 - In this paper, we present a system for information retrieval of biomedical texts at passage level. Our system used KL-divergence as the underlying retrieval model. We further added query expansion and performed post-processing on the results. We were able to obtain a Document MAP of 0.3563, Passage MAP of 0.0464 and Aspect MAP of 0.2255 on one of the three runs.

AB - In this paper, we present a system for information retrieval of biomedical texts at passage level. Our system used KL-divergence as the underlying retrieval model. We further added query expansion and performed post-processing on the results. We were able to obtain a Document MAP of 0.3563, Passage MAP of 0.0464 and Aspect MAP of 0.2255 on one of the three runs.

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