Papers with named entity recognition models
SeqAttack: On Adversarial Attacks for Named Entity Recognition (2021.emnlp-demo)
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| Challenge: | Named Entity Recognition (NER) is a task of recognizing named entities in a chunk of text. |
| Approach: | They investigate the portability of adversarial attacks from text classification to named entity recognition and the ability of adversary training to counteract these attacks. |
| Outcome: | The proposed framework and web application can be used to cherry pick adversarial examples and perform character-level and word-level attacks. |
Named Entity Recognition in a Very Homogenous Domain (2023.findings-eacl)
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| Challenge: | Developing models that perform well on several domains is important, but domain is vague and can be adapted to a new domain. |
| Approach: | They find that even news articles from the same newspaper in English can be considered different domains. |
| Outcome: | The proposed model performs better on out-of-domain data than on specialized data. |
GGPONC 2.0 - The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers (2022.lrec-1)
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Florian Borchert, Christina Lohr, Luise Modersohn, Jonas Witt, Thomas Langer, Markus Follmann, Matthias Gietzelt, Bert Arnrich, Udo Hahn, Matthieu-P. Schapranow
| Challenge: | despite advances in language resources, there is still a shortage of annotated corpora covering (German) medical language. |
| Approach: | They propose to build on clinical guidelines with an annotation scheme based on SNOMED CT . they also train named entity recognition models on the new data set . |
| Outcome: | The new corpus can be built upon clinical guidelines with reasonable coverage of medical terminology. |
Intriguing Properties of Compression on Multilingual Models (2022.emnlp-main)
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Kelechi Ogueji, Orevaoghene Ahia, Gbemileke Onilude, Sebastian Gehrmann, Sara Hooker, Julia Kreutzer
| Challenge: | Multilingual models are dependent on scaling to generalize to a growing number of languages . compression techniques can have disparate effects on model performance for low-resource languages if used sparsely . |
| Approach: | They propose to characterize the impact of sparsifying multilingual pre-trained language models during fine-tuning. |
| Outcome: | The proposed framework characterizes the impact of sparsifying multilingual pre-trained language models during fine-tuning. |
Soft Gazetteers for Low-Resource Named Entity Recognition (2020.acl-main)
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| Challenge: | Existing named entity recognition models use gazetteers to improve performance, but they are limited in coverage and do not exist in low-resource languages. |
| Approach: | They propose a method that integrates Wikipedia information into named entity models by cross-lingual entity linking. |
| Outcome: | The proposed method improves on four low-resource languages with Wikipedia . it incorporates available information from english knowledge bases into neural models . |
Alignment Precedes Fusion: Open-Vocabulary Named Entity Recognition as Context-Type Semantic Matching (2023.findings-emnlp)
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| Challenge: | Continual learning and zero-shot learning approaches have not been adopted to scale to novel-emerging types. |
| Approach: | They propose a method to recognize entities in novel types by their textual names or descriptions. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on three challenging OVNER benchmarks by 9.7%, 9.5%, and 1.8% F1-score of novel types. |
UkraiNER: A New Corpus and Annotation Scheme towards Comprehensive Entity Recognition (2024.lrec-main)
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| Challenge: | Named entity recognition excludes nested, discontinuous, non-named entities in practice . despite attempts to broaden their coverage, the most restrictive variant of NER remains the default . |
| Approach: | They propose a new annotation scheme that offers higher comprehensiveness while preserving simplicity. |
| Outcome: | The proposed scheme offers higher comprehensiveness while preserving simplicity . it also includes an annotation tool to implement the scheme on the corpus UkraiNER . |