Recognition of protein/gene names from text using an ensemble of classifiers
<p>Abstract</p> <p>This paper proposes an ensemble of classifiers for biomedical name recognition in which three classifiers, one Support Vector Machine and two discriminative Hidden Markov Models, are combined effectively using a simple majority voting strategy. In addition, we in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2005-05-01
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| Series: | BMC Bioinformatics |
| _version_ | 1819117600688308224 |
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| author | Zhou GuoDong Shen Dan Zhang Jie Su Jian Tan SoonHeng |
| author_facet | Zhou GuoDong Shen Dan Zhang Jie Su Jian Tan SoonHeng |
| author_sort | Zhou GuoDong |
| collection | DOAJ |
| description | <p>Abstract</p> <p>This paper proposes an ensemble of classifiers for biomedical name recognition in which three classifiers, one Support Vector Machine and two discriminative Hidden Markov Models, are combined effectively using a simple majority voting strategy. In addition, we incorporate three post-processing modules, including an abbreviation resolution module, a protein/gene name refinement module and a simple dictionary matching module, into the system to further improve the performance. Evaluation shows that our system achieves the best performance from among 10 systems with a balanced F-measure of 82.58 on the closed evaluation of the BioCreative protein/gene name recognitiontask (Task 1A).</p> |
| first_indexed | 2024-12-22T05:35:34Z |
| format | Article |
| id | doaj.art-981a8ae0b46241c496a5ec6bf413bb66 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-22T05:35:34Z |
| publishDate | 2005-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-981a8ae0b46241c496a5ec6bf413bb662022-12-21T18:37:20ZengBMCBMC Bioinformatics1471-21052005-05-016Suppl 1S710.1186/1471-2105-6-S1-S7Recognition of protein/gene names from text using an ensemble of classifiersZhou GuoDongShen DanZhang JieSu JianTan SoonHeng<p>Abstract</p> <p>This paper proposes an ensemble of classifiers for biomedical name recognition in which three classifiers, one Support Vector Machine and two discriminative Hidden Markov Models, are combined effectively using a simple majority voting strategy. In addition, we incorporate three post-processing modules, including an abbreviation resolution module, a protein/gene name refinement module and a simple dictionary matching module, into the system to further improve the performance. Evaluation shows that our system achieves the best performance from among 10 systems with a balanced F-measure of 82.58 on the closed evaluation of the BioCreative protein/gene name recognitiontask (Task 1A).</p> |
| spellingShingle | Zhou GuoDong Shen Dan Zhang Jie Su Jian Tan SoonHeng Recognition of protein/gene names from text using an ensemble of classifiers BMC Bioinformatics |
| title | Recognition of protein/gene names from text using an ensemble of classifiers |
| title_full | Recognition of protein/gene names from text using an ensemble of classifiers |
| title_fullStr | Recognition of protein/gene names from text using an ensemble of classifiers |
| title_full_unstemmed | Recognition of protein/gene names from text using an ensemble of classifiers |
| title_short | Recognition of protein/gene names from text using an ensemble of classifiers |
| title_sort | recognition of protein gene names from text using an ensemble of classifiers |
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