Chinese Named Entity Recognition with Graph-based Semi-supervised Learning Model
Aaron Li-Feng Han, Xiaodong Zeng, Derek F. Wong, Lidia Sam Chao · 2015
Named entity recognition (NER) plays an important role in the NLP literature.The traditional methods tend to employ large annotated corpus to achieve a high performance.Different with many semi-supervised learning models for NER task, in this paper, we employ the graph-based semi-supervised learning (GBSSL) method to utilize the freely available unlabeled data.The experiment shows that the unlabeled corpus can enhance the state-of-theart conditional random field (CRF) learning model and has potential to improve the tagging accuracy even though the margin is a little weak and not satisfying in current experiments.