Deep Active Learning for Named Entity Recognition

Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning. While active learning is sample-efficient, it can be computationally expensive since it requires iterative retraining. To speed this up, we introduce a lightweight architecture for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and word encoders and a long short term memory (LSTM) tag decoder. The model achieves nearly state-of-the-art performance on standard datasets for the task while being computationally much more efficient than best performing models. We carry out incremental active learning, during the training process, and are able to nearly match state-of-the-art performance with just 25\% of the original training data

Minimizing Human Labelling Effort for Annotating Named Entities in Historical Newspaper

To accelerate the annotation of named entities (NEs) in historical newspapers like Sarawak Gazette, only two choices are possible: an automatic approach or a semi-automatic approach. This paper presents a fully automatic annotation of NEs occurring in Sarawak Gazette. At the initial stage, a subset of the historical newspapers is fed to an established rule-based named entity recognizer (NER), that is ANNIE. Then, the preannotated corpus is used as training and testing data for three supervised learning NER, which are based on Naïve Bayes, J48 decision trees, and SVM-SMO methods. These methods are not always accurate and it appears that SVM-SMO and J48 have better performance than Naïve Bayes. Thus, a thorough study on the errors done by SVM-SMO and J48 yield to the creation of ad hoc rules to correct the errors automatically. The proposed approach is promising even though it still needs more experiments to refine the rules