A feature-based model for nested named-entity recognition at VLSP-2018 ner evaluation campaign

Abstract

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