Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition

By E. Tjong Kim Sang, Fien De Meulder. 2003

4,732 citations recorded by the source corpus.

We describe the CoNLL-2003 shared task: language-independent named entity recognition. We give background information on the data sets (English and German) and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance.

Subfields: Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Graham Neubig
2. Mohit Bansal
3. Percy Liang
4. Jianfeng Gao
5. Luke S. Zettlemoyer
6. Christopher D. Manning
7. Noah A. Smith
8. Richard Socher

In Evaluation & Benchmarks

1. Mohit Bansal
2. Matt Gardner
3. Luke S. Zettlemoyer
4. Christopher D. Manning
5. Jianfeng Gao
6. Yonatan Belinkov
7. Noah A. Smith
8. Hannaneh Hajishirzi

Citations in the corpus

• Dissecting Contextual Word Embeddings: Architecture and Representation (2018, methodology): “The CoNLL 2003 NER task (Sang and Meulder, 2003) provides entity annotations for approximately 20K sentences from the Reuters RCV1 news corpus.”
• Deep Contextualized Word Representations (2018, methodology): “Named entity extraction The CoNLL 2003 NER task (Sang and Meulder, 2003) consists of”
• REALM: Retrieval-Augmented Language Model Pre-Training (2020, methodology): “We use a BERT-based tagger trained on CoNLL-2003 data (Sang & De Meulder, 2003) to identify named entities, and a regular expression to identify dates.”
• To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks (2019, methodology): “NER We use the CoNLL 2003 dataset (Sang and Meulder, 2003), which provides token level annotations of newswire across four different entity types (PER, LOC, ORG, MISC).”
• Time-Aware Language Models as Temporal Knowledge Bases (2021, methodology): “Following Guu et al. (2020), we identify named entities using a BERT-based tagger trained on CoNLL-2003 data (Tjong Kim Sang and De Meulder, 2003) and a regular expression for dates.”
• GLM-130B: An Open Bilingual Pre-trained Model (2022, methodology): “We design two different types of tasks based on named entity recognition datasets CoNLL03 (Sang & Meulder, 2003), OntoNotes 5.0 (Pradhan et al., 2013), and GENIA (Ohta et al., 2002).”
• LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention (2023, methodology): “We adopt CoNLL03 [56], CoNLL04 [5], CoNLL05 [6], and CoNLL12 [49] as the evaluation datasets.”