# IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition using Knowledge Bases

arXiv: https://arxiv.org/abs/2304.10637 · HTML: https://mlsanity.com/paper/2304.10637/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Ultra-Fine Entity Typing](https://mlsanity.com/paper/1807.04905/) (2018)
- [Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning](https://mlsanity.com/paper/2105.03654/) (2021)
- [UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification](https://mlsanity.com/paper/1809.01479/) (2018)
- [Multilingual Autoregressive Entity Linking](https://mlsanity.com/paper/2103.12528/) (2021)
- [Fast and Accurate Entity Recognition with Iterated Dilated Convolutions](https://mlsanity.com/paper/1702.02098/) (2017)
- [Coarse-to-Fine Pre-training for Named Entity Recognition](https://mlsanity.com/paper/2010.08210/) (2020)
- [Generalisation in Named Entity Recognition: A Quantitative Analysis](https://mlsanity.com/paper/1701.02877/) (2017)
- [Multi-Grained Named Entity Recognition](https://mlsanity.com/paper/1906.08449/) (2019)
- [Zero-Shot Open Entity Typing as Type-Compatible Grounding](https://mlsanity.com/paper/1907.03228/) (2019)
- [MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition](https://mlsanity.com/paper/2208.14536/) (2022)
- [DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition](https://mlsanity.com/paper/2203.00545/) (2022)
- [USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition](https://mlsanity.com/paper/2203.03216/) (2022)
- [Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task](https://mlsanity.com/paper/2204.07459/) (2022)

## Built on by (most cited first, 1 of 1)

- [SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)](https://mlsanity.com/paper/2305.06586/) (2023)

## Abstract (arXiv)

Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance. However, standard benchmarks like CoNLL 2003 do not address many of the challenges that deployed NER systems face, such as having to classify emerging or complex entities in a fine-grained way. In this paper we present a novel NER cascade approach comprising three steps: first, identifying candidate entities in the input sentence; second, linking the each candidate to an existing knowledge base; third, predicting the fine-grained category for each entity candidate. We empirically demonstrate the significance of external knowledge bases in accurately classifying fine-grained and emerging entities. Our system exhibits robust performance in the MultiCoNER2 shared task, even in the low-resource language setting where we leverage knowledge bases of high-resource languages.