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• W2552553554 abstract "Sequence labeling architectures use word embeddings for capturing similarity, but suffer when handling previously unseen or rare words. We investigate character-level extensions to such models and propose a novel architecture for combining alternative word representations. By using an attention mechanism, the model is able to dynamically decide how much information to use from a word- or character-level component. We evaluated different architectures on a range of sequence labeling datasets, and character-level extensions were found to improve performance on every benchmark. In addition, the proposed attention-based architecture delivered the best results even with a smaller number of trainable parameters." @default.
• W2552553554 created "2016-11-30" @default.
• W2552553554 creator A5062209641 @default.
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• W2552553554 date "2016-01-01" @default.
• W2552553554 modified "2023-09-27" @default.
• W2552553554 title "Attending to Characters in Neural Sequence Labeling Models" @default.
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• W2552553554 doi "https://doi.org/10.17863/cam.21366" @default.
• W2552553554 hasPublicationYear "2016" @default.
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