A Comparative Study of Named Entity Recognition on Myanmar Language

2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA) Pub Date : 2020-11-05 DOI:10.1109/O-COCOSDA50338.2020.9295004

Tin Latt Nandar, Thinn Lai Soe, K. Soe

引用次数: 1

Abstract

This paper represents the development of the Myanmar Named Entity Recognition (NER) system using Conditional Random Fields (CRFs). In order to develop the system, a manually annotated Named Entities (NEs) corpus - collected from Myanmar news websites and Asia Language Treebank(ALT)-Parallel-Corpus has been used. We compare the performance of the system getting syllable-based input to the one getting character-based input. We observed that training data has more impact on the performance of the system. The experimental results show that the syllable-based system performs better than the character-based system. It achieves that Precision, Recall and F1-score values of 93.62%, 91.64% and 92.62% respectively.

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缅甸语命名实体识别的比较研究

本文介绍了使用条件随机场(CRFs)的缅甸命名实体识别(NER)系统的发展。为了开发该系统，使用了人工注释的命名实体(NEs)语料库-收集自缅甸新闻网站和亚洲语言树库(ALT)-平行语料库。我们比较了基于音节输入的系统和基于字符输入的系统的性能。我们观察到训练数据对系统性能的影响更大。实验结果表明，基于音节的系统比基于字符的系统性能更好。准确率(Precision)为93.62%，召回率(Recall)为91.64%，F1-score为92.62%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)

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