Chinese Terminology Extraction Using EM-Based Transfer Learning Method
Chinese Terminology Extraction Using EM-Based Transfer Learning Method
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DOI：
10.1007/978-3-642-37247-6_12
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发表时间：
2013-03
期刊：
影响因子：
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通讯作者：
Yanxia Qin;Dequan Zheng;T. Zhao;Min Zhang
中科院分区：
文献类型：
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作者：
Yanxia Qin;Dequan Zheng;T. Zhao;Min Zhang
术语抽取作为信息抽取的重要组成部分，越来越受到人们的关注。目前，统计和基于规则的方法用于提取特定领域中的术语。然而，跨领域术语抽取问题还没有得到很好的解决。本文提出了一种基于电磁法的跨领域中文术语提取的迁移学习方法。首先，从源域学习朴素贝叶斯模型；然后利用基于迁移学习算法将从源域学习到的分类器适应于与源域不同数据分布和域的目标域。该方法的优点是使目标领域能够利用源领域的知识。计算机域和环境域的实验结果表明，基于迁移学习的中文术语提取方法明显优于传统的统计术语提取方法。
As an important part of information extraction, terminology extraction attracts more attention. Currently, statistical and rule-based methods are used to extract terminologies in a specific domain. However, cross-domain terminology extraction task has not been well addressed yet. In this paper we propose using EM-based transfer learning method for cross-domain Chinese terminology extraction. Firstly, a naive bayes model is learned from source domain. Then EM-based transfer learning algorithm is used to adapt the classifier learnt from source domain to target domain, which is in different data distribution and domain from source domain. The advantage of our proposed method is to enable the target domain to utilize the knowledge from the source domain. Experimental results between computer domain and environment domain show the proposed Chinese terminology extraction with EM-based transfer learning method outperforms traditional statistical terminology extraction method significantly.