Semi-Supervised Learning for Named Entity Recognition Using Weakly Labeled Training Data

The shortage of the annotated training data is still an important challenge to building many Natural Language Process (NLP) tasks such as Named Entity Recognition. NER requires a large amount of training data with a high degree of human supervision whereas there is not enough labeled data for every language. In this paper, we use an unlabeled bilingual corpora to extract useful features from transferring information from resource-rich language toward resource-poor language and by using these features and a small training data, make a NER supervised model. Then we utilize a graph-based semi-supervised learning method that trains a CRF-based supervised classifier using that labeled data and uses high-confidence predictions on the unlabeled data to expand the training set and improve efficiency of NER model with the new training set.