Named Entity Recognition on Indonesian Tweets using Hidden Markov Model

被引：0
|
作者：
Azarine, Indira Suri [1 ]
Bijaksana, Moch Arif [1 ]
Asror, Ibnu [1 ]
机构：
[1] Telkom Univ, Sch Comp, Bandung, Indonesia
来源：
2019 7TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY (ICOICT) | 2019年
关键词：
Named Entity Recognition; Tweet; Hidden Markov Model;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Tweets are short messages that can load up to 140 characters, posted by Twitter users. To meet this limit, users usually use abbreviation to express their thoughts, so that produce unstructured and inappropriate sentences grammar. As a result, it is difficult to identify which entity are needed, such as people's names, locations, and organizations. On this research, developed an entity named recognition system on Indonesian-language tweets using Hidden Markov Model with the addition of the POS Tag feature. Using metrics evaluation, the biggest F-1 score is 64.06%. Our source code and data are available at: https://github.com/dirazarine/Named-Entity-Recognition-on-Indonesian-Tweets-using-Hidden-Markov-Model
引用
收藏
页码：547 / 551
页数：5
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