Adversarial Named Entity Recognition with POS label embedding

Abstract

Named Entity Recognition (NER) is dedicated to recognizing different types of named entity. Previous works have shown that part-of-speech, as an important feature, provides complementary syntactical information to NER systems. However, these studies suffer from two limitations: (i) the previous models do not consider the noise from part-of-speech; (ii) the previous models need to re-extract features from token representations. In this paper, we propose a novel approach that can alleviate the above issues as well as make full use of part-of-speech features via attention mechanism and adversarial training. We evaluate our model on three NER datasets, and the experimental results demonstrate that our model achieves a state-of-the-art F1-score of Twitter dataset while matching a state-of-the-art performance on the CoNLL-2003 and Weibo datasets.