A Chinese Term Extraction System in New Energy Vehicles Domain
He Yu1, Lv Xueqiang1, Xu Liping2
1 Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing InformationScience & Technology University, Beijing 100101, China;
2 Beijing Research Center of Urban System Engineering, Beijing 100089, China
[Objective] The problem of Chinese term extraction in new energy vehicles domain is a key problem which needs a special method to improve the precision and recall rate. [Methods] This paper uses conditional random fields model as extraction model, select the word, word length, part of speech, dependencies, dictionary location, stop words and other characteristics as the feature templates. [Results] Experimental results show that the precision and recall are 93.12% and 90.47% respectively. This method improves the performance by 7.73% when compared with the baseline in terms of accuracy. [Limitations] This method can only improve part of the accuracy of the results. [Conclusions] Dependency as one of the conditional random fields model features can improve the precision and recall rate in new energy vehicles domain.
何宇, 吕学强, 徐丽萍. 新能源汽车领域中文术语抽取方法[J]. 现代图书情报技术, 2015, 31(10): 88-94.
He Yu, Lv Xueqiang, Xu Liping. A Chinese Term Extraction System in New Energy Vehicles Domain. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2015.10.12.
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