Abstract
Term is the linguistic expression of the concepts in professional knowledge, which are accumulated through incremental exploration and research in specific fields. In the study of intelligence analysis and knowledge organization, term extraction is an important research subject. Deep neural network is an algorithm based on machine learning. It aims to obtain high-level features that can better represent raw data through learning by multilayer structure. Though machine learning has been widely used in studies in many fields, it is rarely mentioned in term extraction. The paper combines traditional method of extraction term with the new method of machine learning that is deep neural network. And it uses the method to extract terms from the real and effective corpus for experiments. Compared with methods based solely on language rules, language rules & statistical calculation, this method can improve the accuracy rate by about 47% and 8% respectively. This method gets some new terms that are not contained in the thesaurus. It verifies the effectiveness of machine learning in term extraction.
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Acknowledgment
This research is supported by the National Social Science Fund Project: Research on Information Analysis Method and Integrated Platform Based on Fact-type Scientific and Technical Big Data. [grant number 14BTQ038].
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Zeng, W., Li, X., Li, H. (2018). Study on Chinese Term Extraction Method Based on Machine Learning. In: Zhou, Q., Miao, Q., Wang, H., Xie, W., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 902. Springer, Singapore. https://doi.org/10.1007/978-981-13-2206-8_12
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DOI: https://doi.org/10.1007/978-981-13-2206-8_12
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