Entity Extraction in Biochemical Text using Multiobjective Optimization
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| Format: | Artículo científico |
| Language: | en |
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Instituto Politécnico Nacional
2014
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| _version_ | 1876456053158182912 |
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| author | Utpal Kumar Sikdar |
| author_facet | Utpal Kumar Sikdar |
| contents | Entity Extraction in Biochemical Text using Multiobjective Optimization Utpal Kumar Sikdar Asif Ekbal Sriparna Saha Computación condi condi feature selection named entity (NE) named entity (NE) In this paper we propose a multiobjective modified differential evolution based feature selection and classifier ensemble approach for biochemical entity extraction. The algorithm performs in two layers. The first layer concerns with determining an appropriate set of features for the task within the framework of a super- vised statistical classifier, namely, Conditional Random Field (CRF). This produces a set of solutions, a subset of which is used to construct an ensemble in the second layer. The proposed approach is evaluated for entity ex- traction in chemical texts, which involves identification of IUPAC and IUPAC-like names and classification of them into some predefined categories. Experiments that were carried out on a benchmark dataset show the recall, precision and F-measure values of 86.15%, 91.29% and 88.64%, respectively. 2014 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61532067013 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.18 |
| format | Artículo científico |
| id | redalyc_61532067013 |
| institution | Redalyc |
| language | en |
| publishDate | 2014 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Entity Extraction in Biochemical Text using Multiobjective Optimization Utpal Kumar Sikdar Computación condi condi feature selection named entity (NE) named entity (NE) Entity Extraction in Biochemical Text using Multiobjective Optimization Utpal Kumar Sikdar Asif Ekbal Sriparna Saha Computación condi condi feature selection named entity (NE) named entity (NE) In this paper we propose a multiobjective modified differential evolution based feature selection and classifier ensemble approach for biochemical entity extraction. The algorithm performs in two layers. The first layer concerns with determining an appropriate set of features for the task within the framework of a super- vised statistical classifier, namely, Conditional Random Field (CRF). This produces a set of solutions, a subset of which is used to construct an ensemble in the second layer. The proposed approach is evaluated for entity ex- traction in chemical texts, which involves identification of IUPAC and IUPAC-like names and classification of them into some predefined categories. Experiments that were carried out on a benchmark dataset show the recall, precision and F-measure values of 86.15%, 91.29% and 88.64%, respectively. 2014 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61532067013 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.18 |
| title | Entity Extraction in Biochemical Text using Multiobjective Optimization |
| topic | Computación condi condi feature selection named entity (NE) named entity (NE) |
| url | https://www.redalyc.org/articulo.oa?id=61532067013 |