YNNER: Yi Language Named Entity Recognition Dataset

DOI

10.2991/978-2-38476-277-4_170How to use a DOI?

Keywords

Natural language processing; Yi language; Deep learning; Named entity recognition

Abstract

Named entity recognition is an important task in the field of natural language processing, used to identify entities in text and classify them into predefined types. The research on Yi language named entity recognition is still in its early stages both domestically and internationally. Currently, there is no publicly available comprehensive dataset for Yi language named entity recognition, which has hindered the progress in this field. This paper constructed a named entity recognition dataset (Yi language news named entity recognition, YNNER), and manually annotates the names of person, places, and institutions. Then, the named entity recognition model is used to carry out experimental and comparative analysis on the dataset. The experimental results show that the F₁ values of all models are above 70%, which proved the validity and availability of the dataset constructed. This paper aims to promote the research and development of Yi language named entity recognition, provide dataset and baseline models for this field, and expand related research.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Chengxian Wang
PY  - 2024
DA  - 2024/09/02
TI  - YNNER: Yi Language Named Entity Recognition Dataset
BT  - Proceedings of the 2024 10th International Conference on Humanities and Social Science Research (ICHSSR 2024)
PB  - Atlantis Press
SP  - 1520
EP  - 1532
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-277-4_170
DO  - 10.2991/978-2-38476-277-4_170
ID  - Wang2024
ER  -