Sentence Function Recognition Based on Active Learning
Guo Chen1,2(),Tianxiang Xu1
1School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China 2Jiangsu Science and Technology Collaborative Innovation Center of Social Public Safety, Nanjing 210094, China
[Objective] This paper uses active learning methods, structured abstracts and a few annotations to create a classification model for sentence functions, aiming to reduce the dependence on manually labeled corpus. [Methods] First, we trained the SVM, CNN and Bi-LSTM classifiers with structured function sentences from abstracts. With the help of active learning techniques, we predicted the function of a large number of unlabeled common abstract sentences. Third, we automatically identified uncertain samples for manual annotation, which were used to optimize the initial classifier. Finally, we used active learning to improve the performance of classifiers. [Results] We examined the new method with Library and Information Science literature. The precision, recall, and F1 values were 84.65%, 84.49%, and 84.57%, which were 3.25%, 3.24%, and 3.25% higher than those of the traditional methods. [Limitations] We only conducted five iterations to avoid massive work of manual corpus annotation. [Conclusions] Active learning method could effectively discover the difference between unlabeled corpus and existing training corpus, which also reduces the manual labeling costs. The proposed method might be used in citation and full text analysis.
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