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

• URL: http://arxiv.org/abs/2203.00545v1
• Date: Tue, 1 Mar 2022 15:29:35 GMT
• Title: DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition
• Authors: Xinyu Wang, Yongliang Shen, Jiong Cai, Tao Wang, Xiaobin Wang, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang, Kewei Tu, Wei Lu, Yong Jiang
• Abstract summary: MultiCoNER aims at detecting semantically ambiguous named entities in short and low-context settings for multiple languages. Our team DAMO-NLP proposes a knowledge-based system, where we build a multilingual knowledge base based on Wikipedia. Given an input sentence, our system effectively retrieves related contexts from the knowledge base. Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
• Score: 94.1865071914727
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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