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Nested Biomedical Named Entity Recognition [PDF]
International Journal of Intelligent Computing and Information Sciences, 2022 Named entity recognition has been regarded as an important task in natural language processing. Extracting biomedical entities such as RNAs, DNAs, cell lines, proteins, and cell types has been recognized as a challenging task.
Nagwa Badr, yasmine afify, Lobna Mady
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Survey of Chinese Named Entity Recognition [PDF]
Jisuanji kexue yu tansuo, 2022 The Chinese named entity recognition (NER) task is a sub-task within the information extraction domain, where the task goal is to find, identify and classify relevant entities, such as names of people, places and organizations, from sentences given a ...
ZHAO Shan, LUO Rui, CAI Zhiping
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Few-NERD: A Few-shot Named Entity Recognition Dataset [PDF]
Annual Meeting of the Association for Computational Linguistics, 2021 Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER
Ning Ding +7 more
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GPT-NER: Named Entity Recognition via Large Language Models [PDF]
North American Chapter of the Association for Computational Linguistics, 2023 Despite the fact that large-scale Language Models (LLM) have achieved SOTA performances on a variety of NLP tasks, its performance on NER is still significantly below supervised baselines.
Shuhe Wang +7 more
semanticscholar +1 more source
Unified Named Entity Recognition as Word-Word Relation Classification [PDF]
AAAI Conference on Artificial Intelligence, 2021 So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied individually.
Jingye Li +7 more
semanticscholar +1 more source
Template-Based Named Entity Recognition Using BART [PDF]
Findings, 2021 There is a recent interest in investigating few-shot NER, where the low-resource target domain has different label sets compared with a resource-rich source domain. Existing methods use a similarity-based metric.
Leyang Cui +4 more
semanticscholar +1 more source
Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition [PDF]
Conference on Computational Natural Language Learning, 2003 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 ...
E. Tjong Kim Sang, F. D. Meulder
semanticscholar +1 more source
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition [PDF]
International Conference on Learning Representations, 2023 Large language models (LLMs) have demonstrated remarkable generalizability, such as understanding arbitrary entities and relations. Instruction tuning has proven effective for distilling LLMs into more cost-efficient models such as Alpaca and Vicuna. Yet
Wenxuan Zhou +4 more
semanticscholar +1 more source
Named entity recognition in Wikipedia [PDF]
Proceedings of the 2009 Workshop on The People's Web Meets NLP Collaboratively Constructed Semantic Resources - People's Web '09, 2009 Named entity recognition (NER) is used in many domains beyond the newswire text that comprises current gold-standard corpora. Recent work has used Wikipedia's link structure to automatically generate near gold-standard annotations. Until now, these resources have only been evaluated on newswire corpora or themselves.
Dominic Balasuriya +4 more
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Named entity recognition in resumes
CoRR, 2023 Named entity recognition (NER) is used to extract information from various documents and texts such as names and dates. It is important to extract education and work experience information from resumes in order to filter them. Considering the fact that all information in a resume has to be entered to the companys system manually, automatizing this ...
Ege Kesim, Aysu Deliahmetoglu
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