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Journal of Applied Sciences
  Year: 2014 | Volume: 14 | Issue: 2 | Page No.: 177-182
DOI: 10.3923/jas.2014.177.182
 
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Active Learning Based Semi-automatic Annotation of Event Corpus

Jianfeng Fu, Nianzu Liu and Shuangcheng Wang

Abstract:
In the area of Natural Language Processing, building corpus by hand was a hard and time-consuming task. Active learning promised to reduce the cost of annotating dataset for it was allowed to choose the data from which it learned. This study presented a semi-automatic annotation method based on active learning for labeling events in Chinese text. Particularly, it focused on uncertainty-based sampling and query-by-committee based sampling algorithm to evaluate which instance was informative and could be labeled by hand in the unlabeled dataset. The selected informative instances were labeled manually for obtaining a more effective classifier. Experimental results not only demonstrated that active learning improved the accuracy of Chinese event annotation, but also showed that it reduced the number of labeling actions dramatically.
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How to cite this article:

Jianfeng Fu, Nianzu Liu and Shuangcheng Wang , 2014. Active Learning Based Semi-automatic Annotation of Event Corpus. Journal of Applied Sciences, 14: 177-182.

DOI: 10.3923/jas.2014.177.182

URL: https://scialert.net/abstract/?doi=jas.2014.177.182

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