Abstract：Existing Chinese term automatic extraction methods focus on the high-frequency characteristics and unithood indicators of terms, while low frequency terms and termhood indicators lack of effective treatment methods. In response to these problems, this paper introduces the background corpus into C-value method and proposes the concepts of word field distribution degree and effective word frequency. Then the paper automatically extracts the terms by calculating EC-value (Effective C-value) of candidate terms, and improves the extraction performance of low-frequency terms combined with the term cluster recognition and mining. The term extraction experiment in the computer field shows that the proposed improved method (EC-value method) can measure the termhood of terms more effectively, and improve the extraction performance of low-frequency terms.
熊李艳, 谭龙, 钟茂生. 基于有效词频的改进C-value自动术语抽取方法[J]. 现代图书情报技术, 2013, 29(9): 54-59.
Xiong Liyan, Tan Long, Zhong Maosheng. An Automatic Term Extraction System of Improved C-value Based on Effective Word Frequency. New Technology of Library and Information Service, 2013, 29(9): 54-59.
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