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- W2767546521 abstract "Named Entity Recognition (NER) is an important basis for the tasks in natural language processing such as relation extraction, entity linking and so on. The common method of existing Chinese NER systems is to use the character sequence as the input, and the intention is to avoid the word segmentation. However, the character sequence cannot express enough semantic information, so that the recognition accuracy of Chinese NER is not as good as western language such as English. To solve this issue, we propose a Chinese NER method based on Character-Word Mixed Embedding (CWME), and the method is in accord with the pipeline of Chinese natural language processing. Our experiments show that incorporating CWME can effectively improve the performance for the Chinese corpus with state-of-the-art neural architectures widely used in NER, and the proposed method yields nearly 9% absolute improvement over previously results." @default.
- W2767546521 created "2017-11-17" @default.
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- W2767546521 date "2017-11-06" @default.
- W2767546521 modified "2023-09-24" @default.
- W2767546521 title "Chinese Named Entity Recognition with Character-Word Mixed Embedding" @default.
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- W2767546521 doi "https://doi.org/10.1145/3132847.3133088" @default.
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