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- W2912392569 abstract "Bacterial named entity recognition is a challenging task in biomedical field. The task is typically modeled as a sequence labeling problem, and existing work mainly adopts discrete models such as CRF (Conditional Random Fields), requiring a large amount of hand-designed features with domain experience. To address this issue, this paper explores a neural network model for the task. We empirically study the effect of word embeddings and character embeddings on the task by extending a CRF baseline using neural networks. Results show the proposed neural network model achieves competitive performance, outperforming the current best discrete model. Meanwhile, the performance can be further improved by integrating neural and discrete features." @default.
- W2912392569 created "2019-02-21" @default.
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- W2912392569 date "2018-12-01" @default.
- W2912392569 modified "2023-10-16" @default.
- W2912392569 title "Neural Networks for Bacterial Named Entity Recognition" @default.
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- W2912392569 doi "https://doi.org/10.1109/bibm.2018.8621206" @default.
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