.png){kind=icon}
.png){kind=icon}
Return
Feature selection techniques for maximum entropy based biomedical named entity recognition
.png){kind=icon}
OA
AI
DOI：10.1016/j.jbi.2008.12.012.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Named entity recognition is an extremely important and fundamental task of biomedical text mining. Biomedical named entities include mentions of proteins, genes. DNA, RNA, etc which often have complex structures, but it is challenging to identify and classify such entities. Machine learning methods like CRF, MEMM and SVM have been widely used for learning to recognize such entities from an annotated corpus. The identification of appropriate feature templates and the selection of the important feature values play a very important role in the success of these methods. In this paper, we provide a study on word clustering and selection based feature reduction approaches for named entity recognition using a maximum entropy classifier. The identification and selection of features are largely done automatically without using domain knowledge. The performance of the system is found to be superior to existing systems which do not use domain knowledge. (C) 2009 Elsevier Inc. All rights reserved.
Keywords:
Biomedical named entity recognition
Feature selection
Feature reduction
Maximum entropy classifier
Machine learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.5
Papers:
3.5K
Citations:
1.9W