Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text
Table 2: The F values of existing models on the WeiboNER dataset. ∗ indicates that the model utilizes external lexicons. ∘ indicates that the model adopts joint learning. The previous models do not use the BERT, so we show the results of our model without BERT.
['Models', 'NAM', 'NOM', 'Overall']
Our model UIcwsNN significantly outperforms other models and achieves new state-of-the-art performance. The overall score of our model is generally more than 2% higher than the scores of other models. Many methods use lexicon instead of the CWS to provide extractors with external word-level information, but how to choose the appropriate words based on sentence contexts is their challenge. Besides, the approaches that jointly train NER and CWS tasks do not achieve desired results, because segmentation noises affect their effectiveness inevitably. Our model handles this trouble.