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Published in:
Transfer Learning for Chinese-Lao Neural Machine Translation with Linguistic Similarity Read chapter Read first chapter

2020 | OriginalPaper | Chapter

MTNER: A Corpus for Mongolian Tourism Named Entity Recognition

Authors : Xiao Cheng, Weihua Wang, Feilong Bao, Guanglai Gao

Published in: Machine Translation

Publisher: Springer Singapore

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Abstract

Name Entity Recognition is the essential tool for machine translation. Traditional Named Entity Recognition focuses on the person, location and organization names. However, there is still a lack of data to identify travel-related named entities, especially in Mongolian. In this paper, we introduce a newly corpus for Mongolian Tourism Named Entity Recognition (MTNER), consisting of 16,000 sentences annotated with 18 entity types. We trained in-domain BERT representations with the 10 GB of unannotated Mongolian corpus, and trained a NER model based on the BERT tagging model with the newly corpus. Which achieves an overall 82.09 F1 score on Mongolian Tourism Named Entity Recognition and lead to an absolute increase of +3.54 F1 score over the traditional CRF Named Entity Recognition method.

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previous chapter Transfer Learning for Chinese-Lao Neural Machine Translation with Linguistic Similarity
next chapter Unsupervised Machine Translation Quality Estimation in Black-Box Setting
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Metadata
Title
MTNER: A Corpus for Mongolian Tourism Named Entity Recognition
Authors
Xiao Cheng
Weihua Wang
Feilong Bao
Guanglai Gao
Copyright Year
2020
Publisher
Springer Singapore
Book
Machine Translation

Print ISBN: 978-981-336-161-4

Electronic ISBN: 978-981-336-162-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-33-6162-1_2

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