Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning
Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning
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DOI：
10.18653/v1/p16-2025
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Nanyun Peng;Mark Dredze
中科院分区：
文献类型：
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作者：
Nanyun Peng;Mark Dredze
命名实体识别以及其他信息抽取任务经常使用诸如词性标注或组块等语言特征。对于在文本中单词边界不易识别的语言，分词是为命名实体识别系统生成特征的关键的第一步。虽然使用单词边界标签作为特征是有帮助的，但有助于识别这些边界的信号可能会为命名实体识别系统提供更丰富的信息。新的最先进的分词系统使用神经模型来学习用于预测单词边界的表示。我们表明，这些相同的表示与命名实体识别系统联合训练，在中国社交媒体的命名实体识别方面有显著的改进。在我们的实验中，使用长短期记忆网络 - 条件随机场（LSTM - CRF）模型联合训练命名实体识别和分词，比先前公布的结果有近5%的绝对提升。
Named entity recognition, and other information extraction tasks, frequently use linguistic features such as part of speech tags or chunkings. For languages where word boundaries are not readily identified in text, word segmentation is a key first step to generating features for an NER system. While using word boundary tags as features are helpful, the signals that aid in identifying these boundaries may provide richer information for an NER system. New state-of-the-art word segmentation systems use neural models to learn representations for predicting word boundaries. We show that these same representations, jointly trained with an NER system, yield significant improvements in NER for Chinese social media. In our experiments, jointly training NER and word segmentation with an LSTM-CRF model yields nearly 5% absolute improvement over previously published results.