A Kernel-Based Approach for Biomedical Named Entity Recognition
Joint Authors
Patra, Rakesh
Saha, Sujan Kumar
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-29
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Patra, Rakesh& Saha, Sujan Kumar. 2013. A Kernel-Based Approach for Biomedical Named Entity Recognition. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-1013206
Modern Language Association (MLA)
Patra, Rakesh& Saha, Sujan Kumar. A Kernel-Based Approach for Biomedical Named Entity Recognition. The Scientific World Journal No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-1013206
American Medical Association (AMA)
Patra, Rakesh& Saha, Sujan Kumar. A Kernel-Based Approach for Biomedical Named Entity Recognition. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-1013206
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1013206