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Computer Science > Computation and Language

arXiv:1911.07228 (cs)
[Submitted on 17 Nov 2019 (v1), last revised 19 Nov 2019 (this version, v2)]

Title:Error Analysis for Vietnamese Named Entity Recognition on Deep Neural Network Models

Authors:Binh An Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
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Abstract:In recent years, Vietnamese Named Entity Recognition (NER) systems have had a great breakthrough when using Deep Neural Network methods. This paper describes the primary errors of the state-of-the-art NER systems on Vietnamese language. After conducting experiments on BLSTM-CNN-CRF and BLSTM-CRF models with different word embeddings on the Vietnamese NER dataset. This dataset is provided by VLSP in 2016 and used to evaluate most of the current Vietnamese NER systems. We noticed that BLSTM-CNN-CRF gives better results, therefore, we analyze the errors on this model in detail. Our error-analysis results provide us thorough insights in order to increase the performance of NER for the Vietnamese language and improve the quality of the corpus in the future works.
Comments: 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING 2018)
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:1911.07228 [cs.CL]
  (or arXiv:1911.07228v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.1911.07228
Journal reference: 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING 2018)

Submission history

From: Kiet Nguyen Van [view email]
[v1] Sun, 17 Nov 2019 13:03:07 UTC (111 KB)
[v2] Tue, 19 Nov 2019 13:08:38 UTC (36 KB)
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