A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Evaluation Campaign

Minh Quang Nhat Pham


In this report, we describe our participant named-entity recognition system at VLSP 2018 evaluation campaign. We formalized the task as a sequence labeling problem using BIO encoding scheme. We applied a feature-based model which combines word, word-shape features, Brown-cluster-based features, and word-embedding-based features. We compare several methods to deal with nested entities in the dataset. We showed that combining tags of entities at all levels for training a sequence labeling model (joint-tag model) improved the accuracy of nested named-entity recognition.


Nested named-entity recognition, CRF, VLSP

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DOI: https://doi.org/10.15625/1813-9663/34/4/13163 Display counter: Abstract : 566 views. PDF : 245 views.


Journal of Computer Science and Cybernetics ISSN: 1813-9663

Published by Vietnam Academy of Science and Technology