Abstract
The hypernymy detection problem aims to identify the “is-a" relation between words. This problem has recently received attention from researchers in the field of natural language processing because of its application to varied downstream tasks. So far, fairly effective methods for hypernymy detection in English have been reported. In Vietnamese, this problem has not been effectively solved. In this study, we applied a number of hypernymy detection methods based on word embeddings and supervised learning for Vietnamese. We propose an improvement on the dynamic weighting neural network model introduced by Luu Anh Tuan et al. [18] by weighting context words proportionally to the semantic similarity between them and the hypernym. Based on Vietnamese WordNet, three datasets for hypernymy detection were built. Experimental results showed that our proposal can increase the efficiency from 8% to 10% in terms of accuracy compared to the original method.
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Notes
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In this paper, we used ‘_’ characters to associate the syllables of a compound word in Vietnamese.
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Acknowledgments
This paper is a part of project number KHCN-TB.23X/13-18 which is led by Assoc. Prof. Ngo Thanh Quy and funded by Vietnam National University, Hanoi under the Science and Technology Program for the Sustainable Development of Northwest Region.
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Bui, VT., Nguyen, PT., Pham, VL. (2023). Hypernymy Detection for Vietnamese Using Dynamic Weighting Neural Network. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2018. Lecture Notes in Computer Science, vol 13397. Springer, Cham. https://doi.org/10.1007/978-3-031-23804-8_19
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