arXiv Open Access 2022

DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition

Xinyu Wang Yongliang Shen Jiong Cai Tao Wang Xiaobin Wang +7 lainnya

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Abstrak

The MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of contexts makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team DAMO-NLP proposes a knowledge-based system, where we build a multilingual knowledge base based on Wikipedia to provide related context information to the named entity recognition (NER) model. Given an input sentence, our system effectively retrieves related contexts from the knowledge base. The original input sentences are then augmented with such context information, allowing significantly better contextualized token representations to be captured. Our system wins 10 out of 13 tracks in the MultiCoNER shared task.

Topik & Kata Kunci

cs.CL cs.LG

Penulis (12)

Xinyu Wang

Yongliang Shen

Jiong Cai

Tao Wang

Xiaobin Wang

Pengjun Xie

Fei Huang

Weiming Lu

Yueting Zhuang

Kewei Tu

Wei Lu

Yong Jiang

Format Sitasi

APA MLA BibTeX

Wang, X., Shen, Y., Cai, J., Wang, T., Wang, X., Xie, P. et al. (2022). DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition. https://arxiv.org/abs/2203.00545

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓