Method for Automatic Term Extraction from Scientific Articles Based on Weak Supervision
https://doi.org/10.25205/1818-7900-2021-19-2-5-16
Abstract
We propose a method for scientific terms extraction from the texts in Russian based on weakly supervised learning. This approach doesn't require a large amount of hand-labeled data. To implement this method we collected a list of terms in a semi-automatic way and then annotated texts of scientific articles with these terms. These texts we used to train a model. Then we used predictions of this model on another part of the text collection to extend the train set. The second model was trained on both text collections: annotated with a dictionary and by a second model. Obtained results showed that giving additional data, annotated even in an automatic way, improves the quality of scientific terms extraction.
Keywords
term extraction,
neural network language models,
dictionary approach,
weakly supervised learning,
entity recognition,
text processing
Supporting agencies: The study was funded by RFBR according to the research project N 19-07-01134
About the Authors
E. P. Bruches
A.P. Ershov Institute of Informatics Systems SB RAS; Novosibirsk State University
Russian Federation
Russian Federation
Elena P. Bruches - PhD student, A. P. Ershov Institute of Informatics Systems SB RAS; Assistant, Novosibirsk State University.
Novosibirsk.
T. V. Batura
A.P. Ershov Institute of Informatics Systems SB RAS; Novosibirsk State University
Russian Federation
Russian Federation
Tatiana V. Batura - PhD in Physics and Mathematics, Senior Researcher, A. P. Ershov Institute of Informatics Systems SB RAS; Associate Professor, Novosibirsk State University.
Novosibirsk.
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Review
For citations:
Bruches E.P., Batura T.V. Method for Automatic Term Extraction from Scientific Articles Based on Weak Supervision. Vestnik NSU. Series: Information Technologies. 2021;19(2):5-16. (In Russ.) https://doi.org/10.25205/1818-7900-2021-19-2-5-16
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