A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities

Abstract

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%.