Feature-based Neural Language Model and Chinese Word Segmentation
نویسندگان
چکیده
In this paper we introduce a feature-based neural language model, which is trained to estimate the probability of an element given its previous context features. In this way our feature-based language model can learn representation for more sophisticated features. We introduced the deep neural architecture into the Chinese Word Segmentation task. We got a significant improvement on segmenting performance by sharing the pre-learned representation of character features. The experimental result shows that, while using the same feature sets, our neural segmentation model has a better segmenting performance than CRF-based segmentation model.
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