mlsanityData to 7 Oct 2026

USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual Adaptation of Gazetteer for Multilingual Complex NER

arXiv 2305.02517 ↗First posted 4 May 2023cs.CLcited by 2

Not on the map. Why?

Abstract (arXiv)

This paper describes the system developed by the USTC-NELSLIP team for SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II). A method named Statistical Construction and Dual Adaptation of Gazetteer (SCDAG) is proposed for Multilingual Complex NER. The method first utilizes a statistics-based approach to construct a gazetteer. Secondly, the representations of gazetteer networks and language models are adapted by minimizing the KL divergence between them at both the sentence-level and entity-level. Finally, these two networks are then integrated for supervised named entity recognition (NER) training. The proposed method is applied to XLM-R with a gazetteer built from Wikidata, and shows great generalization ability across different tracks. Experimental results and detailed analysis verify the effectiveness of the proposed method. The official results show that our system ranked 1st on one track (Hindi) in this task.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 29 references that are in the map. First posted on arXiv 4 May 2023.

Builds on

15 of 29 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  4. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  5. 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 544
  6. 2019SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimizationcited by 253
  7. 2020Named Entity Recognition as Dependency Parsingcited by 150
  8. 2020Adversarial Training for Large Neural Language Modelscited by 109
  9. 2020RobBERT: a Dutch RoBERTa-based Language Modelcited by 88
  10. 2021Improving Named Entity Recognition by External Context Retrieving and Cooperative Learningcited by 76
  11. 2020ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Searchcited by 34
  12. 2020Soft Gazetteers for Low-Resource Named Entity Recognitioncited by 20
3 more
  1. 2018Hybrid semi-Markov CRF for Neural Sequence Labelingcited by 9
  2. 2014Targetable Named Entity Recognition in Social Mediacited by 8
  3. 2022WIDER & CLOSER: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognitioncited by 7

Built on by

Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)cited by 17
  2. 2023SHINE: Syntax-augmented Hierarchical Interactive Encoder for Zero-shot Cross-lingual Information Extractioncited by 1