Chinese Terminology Extraction Using EM-Based Transfer Learning Method

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.

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