EASAL: Entity-Aware Subsequence-Based Active Learning for Named Entity Recognition

Active learning is a critical technique for reducing labelling load by selecting the most informative data. Most previous works applied active learning on Named Entity Recognition (token-level task) similar to the text classification (sentence-level task). They failed to consider the heterogeneity of uncertainty within each sentence and required access to the entire sentence for the annotator when labelling. To overcome the mentioned limitations, in this paper, we allow the active learning algorithm to query subsequences within sentences and propose an Entity-Aware Subsequences-based Active Learning (EASAL) that utilizes an effective Head-Tail pointer to query one entity-aware subsequence for each sentence based on BERT. For other tokens outside this subsequence, we randomly select 30% of these tokens to be pseudo-labelled for training together where the model directly predicts their pseudo-labels. Experimental results on both news and biomedical datasets demonstrate the effectiveness of our proposed method. The code is released at https://github.com/lylylylylyly/EASAL.

Authors

• Yang Liu College of Computer Science, Sichuan University Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education
• Jinpeng Hu Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
• Zhihong Chen Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
• Xiang Wan Shenzhen Research Institute of Big Data, Shenzhen, China
• Tsung-Hui Chang School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China Shenzhen Research Institute of Big Data, Shenzhen, China

Keywords

• ML: Active Learning
• SNLP: Information Extraction