CRF-based active learning for Chinese named entity recognition

Citations Over TimeTop 25% of 2009 papers

Abstract

Conditional Random Fields (CRFs) have been used for many sequence labeling tasks and got excellent results. Further, the supervised model strongly depends on the huge training data. Active learning is a different way rather than relying on a large amount random sampling. However, random sampling constructively participates in the optimal choosing training examples. Based on different query strategies, active learning can combine with other machine learning methods to reduce the annotation cost while maintaining the accuracy. This paper proposes a new active learning strategy based on Information Density (ID) integrated with CRFs for Chinese Named Entity Recognition (NER). On Sighan bakeoff 2006 MSRA NER corpus, an F1 score of 77.2% is achieved by using only 10,000 labeled training sentences chosen by the proposed active learning strategy.

Related Papers

• → Hybrid semi-Markov CRF for Neural Sequence Labeling(2018)56 cited
• → Biomedical Named Entity Recognition Based on Skip-Chain CRFS(2012)22 cited
• Two Step Chinese Named Entity Recognition Based on Conditional Random Fields Models(2008)
• → Hybrid semi-Markov CRF for Neural Sequence Labeling(2018)3 cited
• → Conditional Random Fields for Biomedical Named Entity Recognition Revisited(2020)