I will give a presentaion about our work from four parts: motivation, methodology, experiment and conclusion. Our work describes the system used in the
SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition, achieving 3rd for track 1 on the leaderboard. We propose Dictionary-fused BERT, a flexible approach for entity dictionaries integration. The main ideas of our systems are: 1) integrating external knowledge (an entity dictionary) into pretrained models to obtain contextualized word and entity representations 2) designing a robust loss function leveraging a logit matrix 3) adding an auxiliary task, which is an on-top
binary classification to decide whether the token is a mention word or not, makes the main task easier to learn. It is worth noting that our system achieves an F1 of 0.914 in the postevaluation stage by updating the entity dictionary to the one of GemNet, which is higher than the score of 1st on the leaderboard of the evaluation stage.


PAI at SemEval-2022 Task 11: Name Entity Recognition with Contextualized Entity Representations and Robust Loss Functions


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W25: SemEval-2022: 15th International Workshop on Semantic Evaluation

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## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Xiaorong Jian

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PAI at SemEval-2022 Task 11: Name Entity Recognition with Contextualized Entity Representations and Robust Loss Functions

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