Adversarial Learning for Chinese NER from Crowd AnnotationsThe corresponding author is Wenliang Chen.
Table 2: Main results on the DL-PS data.
['Model', 'P', 'R', 'F1']
In this section, we show the model performances of our proposed crowdsourcing learning system (ALCrowd), and meanwhile compare it with the other systems mentioned above. In the first case, none of the annotations get the correct NER result, but our proposed model can capture it. The result of LSTM-CRF is the same as majority-voting. The model of LSTM-CRF fails to recognize the named entity “Xiexie” because of not trusting the second annotation, treating it as one noise annotation. Our proposed model is able to recognize it, because of its ability of extracting worker independent features.