A Feature-Rich Vietnamese Named-Entity Recognition Model
This work addresses NER for Vietnamese, a domain-specific task, and is incremental as it builds on existing feature-based methods with a systematic evaluation of NLP toolkits.
The paper tackles the problem of Vietnamese named-entity recognition (NER) by developing a feature-based model using Conditional Random Fields (CRF) with various features, achieving state-of-the-art accuracy for the language.
In this paper, we present a feature-based named-entity recognition (NER) model that achieves the start-of-the-art accuracy for Vietnamese language. We combine word, word-shape features, PoS, chunk, Brown-cluster-based features, and word-embedding-based features in the Conditional Random Fields (CRF) model. We also explore the effects of word segmentation, PoS tagging, and chunking results of many popular Vietnamese NLP toolkits on the accuracy of the proposed feature-based NER model. Up to now, our work is the first work that systematically performs an extrinsic evaluation of basic Vietnamese NLP toolkits on the downstream NER task. Experimental results show that while automatically-generated word segmentation is useful, PoS and chunking information generated by Vietnamese NLP tools does not show their benefits for the proposed feature-based NER model.