A Kernel-Based Approach for Biomedical Named Entity Recognition
Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-s...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/950796 |
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| author | Rakesh Patra Sujan Kumar Saha |
| author_facet | Rakesh Patra Sujan Kumar Saha |
| author_sort | Rakesh Patra |
| collection | DOAJ |
| description | Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy. |
| format | Article |
| id | doaj-art-dbec83ae19184d499b055f72d0cfd7eb |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-dbec83ae19184d499b055f72d0cfd7eb2025-02-03T01:22:07ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/950796950796A Kernel-Based Approach for Biomedical Named Entity RecognitionRakesh Patra0Sujan Kumar Saha1Department of Computer Science & Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, IndiaDepartment of Computer Science & Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, IndiaSupport vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy.http://dx.doi.org/10.1155/2013/950796 |
| spellingShingle | Rakesh Patra Sujan Kumar Saha A Kernel-Based Approach for Biomedical Named Entity Recognition The Scientific World Journal |
| title | A Kernel-Based Approach for Biomedical Named Entity Recognition |
| title_full | A Kernel-Based Approach for Biomedical Named Entity Recognition |
| title_fullStr | A Kernel-Based Approach for Biomedical Named Entity Recognition |
| title_full_unstemmed | A Kernel-Based Approach for Biomedical Named Entity Recognition |
| title_short | A Kernel-Based Approach for Biomedical Named Entity Recognition |
| title_sort | kernel based approach for biomedical named entity recognition |
| url | http://dx.doi.org/10.1155/2013/950796 |
| work_keys_str_mv | AT rakeshpatra akernelbasedapproachforbiomedicalnamedentityrecognition AT sujankumarsaha akernelbasedapproachforbiomedicalnamedentityrecognition AT rakeshpatra kernelbasedapproachforbiomedicalnamedentityrecognition AT sujankumarsaha kernelbasedapproachforbiomedicalnamedentityrecognition |