A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities
Utpal Kumar Sikdar, Bj{\"o}rn Gamb{\"a}ck
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Detecting previously unseen named entities in text is a challenging task. The paper describes how three initial classifier models were built using Conditional Random Fields (CRFs), Support Vector Machines (SVMs) and a Long Short-Term Memory (LSTM) recurrent neural network. The outputs of these three classifiers were then used as features to train another CRF classifier working as an ensemble. 5-fold cross-validation based on training and development data for the emerging and rare named entity recognition shared task showed precision, recall and F1-score of 66.87\%, 46.75\% and 54.97\%, respectively. For surface form evaluation, the CRF ensemble-based system achieved precision, recall and F1 scores of 65.18\%, 45.20\% and 53.30\%. When applied to unseen test data, the model reached 47.92\% precision, 31.97\% recall and 38.55\% F1-score for entity level evaluation, with the corresponding surface form evaluation values of 44.91\%, 30.47\% and 36.31\%.