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Conference proceeding
Promoting Ranking Diversity for Biomedical Information Retrieval based on LDA
2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM 2011), pp 456-461
01 Jan 2011
Abstract
In this paper, we propose an approach based on a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on N-size slide window. Experiments on TREC 2007 Genomics collection and two distinctive IR baseline runs demonstrate the effectiveness of our method in promoting ranking diversity for biomedical information retrieval. Evaluation results show that our approach can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track.
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Details
- Title
- Promoting Ranking Diversity for Biomedical Information Retrieval based on LDA
- Creators
- Yan Chen - University of Chinese Academy of SciencesXiaoshi Yin - Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R ChinaZhoujun Li - National University of Defense TechnologyXiaohua Hu - Drexel University, Information ScienceJimmy Xiangji Huang - York Univ, Sch Informat Technol, Toronto, ON, Canada
- Publication Details
- 2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM 2011), pp 456-461
- Series
- IEEE International Conference on Bioinformatics and Biomedicine-BIBM
- Publisher
- IEEE
- Number of pages
- 6
- Grant note
- SKLSDE-2011ZX-03 / Fund of State Key Laboratory of Software Development Environment 61170189 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000411330600086
- Scopus ID
- 2-s2.0-84862969491
- Other Identifier
- 991019167451604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Medical Informatics