Fine-Grained Named Entity Recognition for Sinhala

被引：0
|
作者：
Azeez, Rameela [1 ]
Ranathunga, Surangika [1 ]
机构：
[1] Univ Moratuwa, Dept Comp Sci & Engn, Katubedda 10400, Sri Lanka
来源：
MERCON 2020: 6TH INTERNATIONAL MULTIDISCIPLINARY MORATUWA ENGINEERING RESEARCH CONFERENCE (MERCON) | 2020年
关键词：
named entity recognition; sinhala; named entity; conditional random fields;
D O I：
10.1109/mercon50084.2020.9185296
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
For English, Named Entity Recognition (NER) is more or less a solved problem. However, for low-resourced and morphologically rich languages such as Sinhala, minimal research has been done. In this paper, we present a novel fine-grained Named Entity (NE) tag set and an NE annotated Sinhala corpus of 70k word tokens. We trained a custom NER model for Sinhala based on Conditional Random Fields (CRF). Despite the low-resourced setting, this NER model could achieve an micro-averaged F1 score of 84.8.
引用
收藏
页码：295 / 300
页数：6
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