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| A Deep Learning Model and Self-Training Algorithm for Theoretical Terms Extraction |
| Zhao Hong, Wang Fang |
| Department of Information Resources Management, Business School, Nankai University, Tianjin 300071 |
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Abstract Extraction of theoretical terminology from literature is a precondition for more than one research field in information science, such as content analysis of large scale literature and interdisciplinary knowledge transfer revelation. As specific types of named entities, theoretical terms are distributed among many subjects and a large section of published literature, have complex characteristics, and lack large-scale mature corpuses, rendering their extraction quite challenging. To improve the extraction performance and reduce the cost of manual tagging for the training set, a deep learning model for theoretical term extraction was built based on the characteristics of the terms and a self-training algorithm aimed at achieving a weak supervised learning of the model; further, the characteristic construction and tagging method in the model were studied. The validities of the model and the self-training algorithm were verified via experimental comparisons. This study not only provides a more effective method for theoretical term extraction but also provides a reference for the recognition of other named entities.
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Received: 02 February 2018
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