Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text

• URL: http://arxiv.org/abs/2004.06384v2
• Date: Mon, 15 Jun 2020 09:10:35 GMT
• Title: Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text
• Authors: Shengbin Jia, Ling Ding, Xiaojun Chen, Shijia E, Yang Xiang
• Abstract summary: segmentation error propagation is a challenge for Chinese named entity recognition systems. We propose a model (UIcwsNN) that specializes in identifying entities from Chinese social media text.
• Score: 18.455836845989523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chinese word segmentation is necessary to provide word-level information for Chinese named entity recognition (NER) systems. However, segmentation error propagation is a challenge for Chinese NER while processing colloquial data like social media text. In this paper, we propose a model (UIcwsNN) that specializes in identifying entities from Chinese social media text, especially by leveraging ambiguous information of word segmentation. Such uncertain information contains all the potential segmentation states of a sentence that provides a channel for the model to infer deep word-level characteristics. We propose a trilogy (i.e., candidate position embedding -> position selective attention -> adaptive word convolution) to encode uncertain word segmentation information and acquire appropriate word-level representation. Experiments results on the social media corpus show that our model alleviates the segmentation error cascading trouble effectively, and achieves a significant performance improvement of more than 2% over previous state-of-the-art methods.

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