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- W2909311579 abstract "In recent years, after the neural-network-based method was proposed, the accuracy of the Chinese word segmentation task has made great progress. However, when dealing with out-of-vocabulary words, there is still a large error rate. We used a simple bidirectional LSTM architecture and a large-scale pretrained language model to generate high-quality contextualize character representations, which successfully reduced the weakness of the ambiguous meanings of each Chinese character that widely appears in Chinese characters, and hence effectively reduced OOV error rate. State-of-the-art performance is achieved on many datasets." @default.
- W2909311579 created "2019-01-25" @default.
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- W2909311579 date "2019-01-17" @default.
- W2909311579 modified "2023-09-27" @default.
- W2909311579 title "Robust Chinese Word Segmentation with Contextualized Word Representations" @default.
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