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- W4317178346 abstract "Word-level information is crucial for Chinese named entity recognition. Presently, most works have achieved better performance by extracting word-level information into character-level representations through existing lexicons, but the maintenance of lexical lists is a major challenge. In this paper, we present the NIMSI model, proposing the incorporation of multiple segmentation information to enhance recognition, using a trilogy to align character-level attention with word-level attention to construct features of segmented information in Chinese text. Also, we use a simple but effective method to directly incorporate multi-segmentation information into character-level representations. Finally, as the experiments on the three benchmark datasets show, our model effectively incorporates segmentation information and alleviates the segmentation errors." @default.
- W4317178346 created "2023-01-18" @default.
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- W4317178346 date "2022-04-21" @default.
- W4317178346 modified "2023-10-18" @default.
- W4317178346 title "Named Entity Recognition Incorporating Chinese Segmentation Information" @default.
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- W4317178346 doi "https://doi.org/10.1109/ieeeconf52377.2022.10013348" @default.
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