LTP A New Active Learning Strategy for CRF Based Named Entity Recognition

In recent years, deep learning has achieved great success in many natural language processing tasks including named entity recognition . The shortcoming is that a large amount of manually-annotated data is usually required . Lowest Token Probability (LTP) combines input and output of CRF to select informative instance . LTP is simple and powerful strategy that does not favor long sequences and does not need to invade the model . We test LTP on multiple datasets, and the experiments show that LTP performs slightly better than traditional strategies with obviously less annotated tokens on both sentence-level accuracy and entity-level F1-score . 

Links: PDF - Abstract

Code :

https://github.com/HIT-ICES/AL-NER

Keywords : ltp - entity - strategy - level - recognition -

Leave a Reply

Your email address will not be published. Required fields are marked *