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  1. Proceedings of the 5th international workshop on Bioinformatics (BIOKDD '05)
  2. Boosting performance of bio-entity recognition by combining results from multiple systems
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Motif discovery for proteins using subsequence clustering
Predicting cancer susceptibility from single-nucleotide polymorphism data: a case study in multiple myeloma
Accelerating DNA sequencing-by-hybridization with noise
Analysis of protein-protein interaction networks using random walks
Graphical models of residue coupling in protein families
On discovery of maximal confident rules without support pruning in microarray data
Finding cliques in protein interaction networks via transitive closure of a weighted graph
A datamining approach to cell population deconvolution from gene expressions using particle filters
Boosting performance of bio-entity recognition by combining results from multiple systems
siRNA off-target search: a hybrid q-gram based filtering approach

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Boosting performance of bio-entity recognition by combining results from multiple systems

Content Provider ACM Digital Library
Author Huang, Xiangji Si, Luo Kanungo, Tapas
Abstract The task of biomedical named-entity recognition is to identify technical terms in the domain of biology that are of special interest to domain experts. While numerous algorithms have been proposed for this task, biomedical named-entity recognition remains a challenging task and an active area of research, as there is still a large accuracy gap between the best algorithms for biomedical named-entity recognition and those for general newswire named-entity recognition. The reason for such discrepancy in accuracy results is generally attributed to inadequate feature representations of individual entity recognition systems and external domain knowledge.In order to take advantage of the rich feature representations and external domain knowledge used by different systems, we propose several Meta biomedical named-entity recognition algorithms that combine recognition results of various recognition systems. The proposed algorithms -- majority vote, unstructured exponential model and conditional random field -- were tested on the GENIA biomedical corpus. Empirical results show that the F score can be improved from 0.72, which is attained by the best individual system, to 0.96 by our Meta entity recognition approach.
Starting Page 76
Ending Page 83
Page Count 8
File Format PDF
ISBN 1595932135
DOI 10.1145/1134030.1134044
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2005-08-21
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Meta recognition Biomedical named-entity recognition
Content Type Text
Resource Type Article
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