홈 › Papers

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

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{\%}.

Code (0)

등록된 구현이 없습니다.

Tasks

Methods 이 논문이 사용한 방법론

Similar Papers 제목 키워드 기반