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A named entity recognition system for Dutch [PDF]
, 2002 We describe a Named Entity Recognition system for Dutch that combines gazetteers, hand-crafted rules, and machine learning on the basis of seed material. We used gazetteers and a corpus to construct training material for Ripper, a rule learner.
Daelemans, Walter +2 more
core +8 more sources
Named Entity Recognition with Bidirectional LSTM-CNNs [PDF]
Transactions of the Association for Computational Linguistics, 2021 Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.
Jason P.C. Chiu, Eric Nichols
doaj +2 more sources
Improving large language models for clinical named entity recognition via prompt engineering. [PDF]
J Am Med Inform Assoc, 2023 Importance The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data.
Hu Y +11 more
europepmc +3 more sources
Enhancing biomedical named entity recognition with parallel boundary detection and category classification [PDF]
BMC BioinformaticsBackground Named entity recognition is a fundamental task in natural language processing. Recognizing entities in biomedical text, known as the BioNER, is particularly crucial for cutting-edge applications.
Yu Wang +4 more
doaj +2 more sources
Review on Named Entity Recognition [PDF]
Jisuanji kexue yu tansuo, 2022 In the field of natural language processing, named entity recognition is the first key step of information extraction. Named entity recognition task aims to recognize named entities from a large number of unstructured texts and classify them into ...
LI Dongmei, LUO Sisi, ZHANG Xiaoping, XU Fu
doaj +1 more source
Multilingual Fine-Grained Named Entity Recognition [PDF]
Computer Science Journal of Moldova, 2023 The “MultiCoNER II Multilingual Complex Named Entity Recognition” task\footnote[1]{\url{https://multiconer.github.io}} within SemEval 2023 competition focuses on identifying complex named entities (NEs), such as the titles of creative works (e.g., songs,
Viorica-Camelia Lupancu, Adrian Iftene
doaj +1 more source
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
semanticscholar +1 more source
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
FloraNER: A new dataset for species and morphological terms named entity recognition in French botanical text [PDF]
Data in BriefFloraNER is a distantly supervised named entity recognition dataset (NER). The dataset is built from botanical French literature extracted from the OCR-preprocessed flora of New Caledonia, provided by the National Museum of Natural History in France ...
Ayoub Nainia +5 more
doaj +2 more sources
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