Papers with CoNLL 2003
A Named Entity Recognition Shootout for German (P18-2)
Copied to clipboard
| Challenge: | Named entity recognition and classification (NER) is a central component in many natural language processing pipelines. |
| Approach: | They propose to build a model for German named entity recognition that performs at the state of the art for both contemporary and historical texts. |
| Outcome: | The proposed model outperforms the CRF and BiLSTM on large and small datasets. |
Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020.acl-main)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) performance often degrades when applied to target domains that differ from the texts observed during training. |
| Approach: | They propose a method to learn NER models in the absence of labelled data through weak supervision by using a broad spectrum of labelling functions to automatically annotate texts from the target domain. |
| Outcome: | The proposed approach improves on two English datasets and shows that it improves by 7 percentage points on entity-level F1 scores compared to an out-of-domain neural NER model. |
Gradient-based Intra-attention Pruning on Pre-trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models are computationally expensive and slow in inference due to their large sizes. |
| Approach: | They propose a structured pruning method which combines pruning with knowledge distillation to yield highly effective models. |
| Outcome: | The proposed method outperforms other pruning methods in sparsity regimes while maintaining 93% 99% performance. |
Joint Neural Entity Disambiguation with Output Space Search (C18-1)
Copied to clipboard
| Challenge: | Existing models for entity disambiguation combine local contextual information and global evidences. |
| Approach: | They propose a limited discrepancy search model that combines local contextual information and global evidences to improve a local solution from a global view point. |
| Outcome: | The proposed model improves local and global solutions on CoNLL 2003 and TAC 2010 benchmarks. |
Subsequence Based Deep Active Learning for Named Entity Recognition (2021.acl-long)
Copied to clipboard
| Challenge: | Active Learning (AL) has been successfully applied to Deep Learning to drastically reduce the amount of data required to achieve high performance. |
| Approach: | They propose to query subsequences within sentences and propagate their labels to other sentences. |
| Outcome: | The proposed approach achieves high performance on OntoNotes 5.0 and CoNLL 2003 with only 13% of training data and 27% of the training data. |
Do “English” Named Entity Recognizers Work Well on Global Englishes? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Most of English named entity recognition datasets contain American or British English data . multiple problems may occur in low-resource English contexts, such as confusion of named entities with regionspecific meanings . |
| Approach: | They build a newswire dataset to analyze NER model performance on low-resource English variants . they find that models trained on the CoNLL or OntoNotes datasets experienced significant performance drops . |
| Outcome: | The results show that models trained on the CoNLL or OntoNotes datasets experienced significant performance drops. |