EconBERTa: Towards Robust Extraction of Named Entities in Economics (2023.findings-emnlp)
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Karim Lasri, Pedro Vitor Quinta de Castro, Mona Schirmer, Luis Eduardo San Martin, Linxi Wang, Tomáš Dulka, Haaya Naushan, John Pougué-Biyong, Arianna Legovini, Samuel Fraiberger
| Challenge: | Adapting general-purpose language models to specific domains has proven to be effective in tackling downstream tasks such as impact evaluation. |
| Approach: | They propose to use EconBERTa, a large language model pretrained on scientific publications in economics, and ECON-IE, based on an expert-annotated dataset of economics abstracts for Named Entity Recognition (NER). |
| Outcome: | The proposed model outperforms EconBERTa on the downstream NER task and ECON-IE on the economics abstracts. |
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| Challenge: | Large Language Models (LLMs) exhibit remarkable adaptability across domains, but they are often not suitable for structured knowledge extraction tasks such as named entity recognition (NER). |
| Approach: | They propose a method that instructs LLMs to self-reflect on the specific domain and generates domain-relevant attributes for creating attribute-rich training data. |
| Outcome: | The proposed method produces NER datasets in domains with domain-relevant attributes and generates entity terms and NER context data around these entities. |
GPT-NER: Named Entity Recognition via Large Language Models (2025.findings-naacl)
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Shuhe Wang, Xiaofei Sun, Xiaoya Li, Rongbin Ouyang, Fei Wu, Tianwei Zhang, Jiwei Li, Guoyin Wang, Chen Guo
| Challenge: | Large-scale language models (LLMs) have shown impressive ability for in-context learning with limited training data. |
| Approach: | They propose a novel sequence labeling task that transforms a sequence labeled as a text-generation task into a self-verification task that LLMs can adapt to. |
| Outcome: | The proposed model performs better on NER than supervised models on a variety of tasks . the proposed model can be easily adapted by LLMs to generate a text sequence . |
NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
| Approach: | They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition . |
| Outcome: | The proposed model matches or surpasses existing models in NER tasks . the proposed model is based on a large, silver-annotated corpus of 4 million paragraphs . |
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction (2023.eacl-main)
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Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, Xiang Ren
| Challenge: | Named entity recognition models have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. |
| Approach: | They propose a framework that automatically generates and leverages “entity triggers” which are human-readable cues in the text that help guide the model to make better decisions. |
| Outcome: | The proposed framework outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on three well-studied datasets. |
Interpretable Multi-dataset Evaluation for Named Entity Recognition (2020.emnlp-main)
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| Challenge: | Existing evaluation methods for named entity recognition tasks are difficult to interpret . authors present a general methodology for interpretable evaluation for named entities . |
| Approach: | They propose a general methodology for interpretable evaluation for named entity recognition task. |
| Outcome: | The proposed evaluation method enables researchers to interpret differences in models and datasets . it makes it easy for future researchers to run similar analyses and drive progress in this area . |
GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) models are crucial for academic writing . existing ground truth datasets do not treat fine-grained types like ML model and model architecture as separate entity types . |
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Exploring Nested Named Entity Recognition with Large Language Models: Methods, Challenges, and Insights (2024.emnlp-main)
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| Challenge: | Named entity recognition (NER) is a challenging task in natural language processing . nested NER requires sophisticated techniques to identify entities within entities . |
| Approach: | They investigate the application of Large Language Models (LLMs) to nested NER . they find methodologies from previous work are less effective . |
| Outcome: | The proposed methods outperform BERT-based models in nested NER tasks . however, they do not outperformed the existing models on the GENIA dataset . |
Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark (2024.naacl-long)
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Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Suppa, Hila Gonen, Joseph Marvin Imperial, Börje Karlsson, Peiqin Lin, Nikola Ljubešić, Lester James Miranda, Barbara Plank, Arij Riabi, Yuval Pinter
| Challenge: | In named entity recognition, the majority of annotation efforts are centered on English, and cross-lingual transfer performance remains brittle. |
| Approach: | They propose to develop gold-standard named entity recognition benchmarks in many languages using a cross-lingual consistent schema. |
| Outcome: | The proposed benchmarks will be released to the public in 2022 . they will provide baselines on in-language and cross-lingual learning settings. |
Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models (2025.acl-long)
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| Challenge: | Existing approaches to align large language models with information extraction tasks are costly and not all training data benefits target domains. |
| Approach: | They propose a framework which dynamically Selects and Merges expert models at inference time and combines experts beneficial to target domains. |
| Outcome: | The proposed framework outperforms the unified model by 10% on multiple benchmarks. |
MasakhaNER: Named Entity Recognition for African Languages (2021.tacl-1)
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David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D’souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, Stephen Mayhew, Israel Abebe Azime, Shamsuddeen H. Muhammad, Chris Chinenye Emezue, Joyce Nakatumba-Nabende, Perez Ogayo, Aremu Anuoluwapo, Catherine Gitau, Derguene Mbaye, Jesujoba Alabi, Seid Muhie Yimam, Tajuddeen Rabiu Gwadabe, Ignatius Ezeani, Rubungo Andre Niyongabo, Jonathan Mukiibi, Verrah Otiende, Iroro Orife, Davis David, Samba Ngom, Tosin Adewumi, Paul Rayson, Mofetoluwa Adeyemi, Gerald Muriuki, Emmanuel Anebi, Chiamaka Chukwuneke, Nkiruka Odu, Eric Peter Wairagala, Samuel Oyerinde, Clemencia Siro, Tobius Saul Bateesa, Temilola Oloyede, Yvonne Wambui, Victor Akinode, Deborah Nabagereka, Maurice Katusiime, Ayodele Awokoya, Mouhamadane MBOUP, Dibora Gebreyohannes, Henok Tilaye, Kelechi Nwaike, Degaga Wolde, Abdoulaye Faye, Blessing Sibanda, Orevaoghene Ahia, Bonaventure F. P. Dossou, Kelechi Ogueji, Thierno Ibrahima DIOP, Abdoulaye Diallo, Adewale Akinfaderin, Tendai Marengereke, Salomey Osei
| Challenge: | (2020) African languages are underrepresented in existing natural language processing datasets, research, and tools due to lack of datasets and reproducible results. |
| Approach: | They propose to create a dataset for named entity recognition (NER) in ten African languages. |
| Outcome: | The results of the first large dataset for named entity recognition (NER) in ten African languages are released to inform future research on African NLP. |