Abstract
Tibetan Word Segmentation is a basic and essential task in Tibetan Natural Language Processing workflow. Performance of TWS can directly affect many other downstream Tibetan NLP tasks since errors propagate in a multi-stage NLP pipeline. Traditionally the majority of researchers leverage linear statistical approaches to tackle Tibetan Word Segmentation, which often requires hand-crafted linguistic feature engineering with great care. In this work, we propose a neural network architecture for Tibetan Word Segmentation, which is a stacked combination of CNN, Bi-LSTM and CRF. By using tagged data for supervised learning and unlabeled data for representation learning, with no involvement in feature engineering, our model can produce promising performance on the test set, surpassing our baseline models by a large margin, and indicating the effectiveness of the proposed neural model.
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Acknowledgments
This work was supported by Science and Technology Department of Qinghai Province (grant numbers: 2020-ZJ-Y05, 2020-ZJ-704) and The National Key Research and Development Program of China (grant number: 2017YFB1402200).
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Duanzhu, S., Jiacuo, C., Jia, C. (2021). Revisiting Tibetan Word Segmentation with Neural Networks. In: Liu, M., Kit, C., Su, Q. (eds) Chinese Lexical Semantics. CLSW 2020. Lecture Notes in Computer Science(), vol 12278. Springer, Cham. https://doi.org/10.1007/978-3-030-81197-6_44
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DOI: https://doi.org/10.1007/978-3-030-81197-6_44
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