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- W2886923263 abstract "Identifying entity boundaries and eliminating entity ambiguity are two major challenges faced by Chinese named entity recognition researches. This paper proposes a five-stroke based CNN-BiRNN-CRF network for Chinese named entity recognition. In terms of input embeddings, we apply five-stroke input method to obtain stroke-level representations, which are concatenated with pre-trained character embeddings, in order to explore the morphological and semantic information of characters. Moreover, the convolutional neural network is used to extract n-gram features, without involving hand-crafted features or domain-specific knowledge. The proposed model is evaluated and compared with the state-of-the-art results on the third SIGHAN bakeoff corpora. The experimental results show that our model achieves 91.67% and 90.68% F1-score on MSRA corpus and CityU corpus separately." @default.
- W2886923263 created "2018-08-22" @default.
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- W2886923263 date "2018-01-01" @default.
- W2886923263 modified "2023-09-25" @default.
- W2886923263 title "Five-Stroke Based CNN-BiRNN-CRF Network for Chinese Named Entity Recognition" @default.
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- W2886923263 doi "https://doi.org/10.1007/978-3-319-99495-6_16" @default.
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