%A He Huixin,Liu Lijuan %T A Scientific Research Object Labeling System Based on Active earning %0 Journal Article %D 2016 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.1003-3513.2016.03.09 %P 67-73 %V 32 %N 3 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4201.shtml} %8 2016-03-25 %X

[Objective] This study aims to identify the research object attribute instance from the paper titles. With the help of limited labeled samples, we could maximumize the accuracy of research object recognition. [Methods] We first analyzed the grammatical features of scientific research objects based on conditional random field sequence labeling algorithm. Second, we recognized and extracted research objects using a small amount of samples. Finally, we introduced an active learning iterative labeling system based on unlabeled data to improve the research object recognition accuracy. [Results] The results showed that the proposed method could efficiently use the unlabeled data, and increase the accuracy of the research object recognition to 78.3%. [Limitations] The proposed algorithm needs to be further optimized to improve its efficiency. [Conclusions] The proposed method performed well on the research object attributes identification, which is the foundation for further mining the knowledge system and the structure of science and technology literature.