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

页码：295 / 300

页数：6

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