Lotus at WojoodNER Shared Task: Multilingual Transformers: Unveiling Flat and Nested Entity Recognition

Authors

Jiyong Li, Sun Yat-Sen University
Dilshod Azizov, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Hilal AlQuabeh, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Shangsong Liang, Sun Yat-Sen University

Document Type

Conference Proceeding

Publication Title

ArabicNLP 2023 - 1st Arabic Natural Language Processing Conference, Proceedings

Abstract

We introduce our systems developed for two subtasks in the shared task “WOJOOD” on Arabic NER detection, part of ARABICNLP 2023. For Subtask 1, we employ the XLM-R model to predict Flat NER labels for given tokens using a single classifier capable of categorizing all labels. For Subtask 2, we use the XLM-R encoder by building 21 individual classifiers. Each classifier corresponds to a specific label and is designed to determine the presence of its respective label. In terms of performance, our systems achieved competitive micro-F1 scores of 0.83 for Subtask 1 and 0.76 for Subtask 2, according to the leaderboard scores.

First Page

765

Last Page

770

Publication Date

1-1-2023

Recommended Citation

J. Li et al., "Lotus at WojoodNER Shared Task: Multilingual Transformers: Unveiling Flat and Nested Entity Recognition," ArabicNLP 2023 - 1st Arabic Natural Language Processing Conference, Proceedings, pp. 765 - 770, Jan 2023.