Gollie: Annotation guidelines improve zero-shot information-extraction

O Sainz, I García-Ferrero, R Agerri… - arXiv preprint arXiv …, 2023 - arxiv.org
Large Language Models (LLMs) combined with instruction tuning have made significant
progress when generalizing to unseen tasks. However, they have been less successful in …

Seqgpt: An out-of-the-box large language model for open domain sequence understanding

T Yu, C Jiang, C Lou, S Huang, X Wang… - Proceedings of the …, 2024 - ojs.aaai.org
Large language models (LLMs) have shown impressive abilities for open-domain NLP
tasks. However, LLMs are sometimes too footloose for natural language understanding …

Arabic fine-grained entity recognition

H Liqreina, M Jarrar, M Khalilia, AO El-Shangiti… - arXiv preprint arXiv …, 2023 - arxiv.org
Traditional NER systems are typically trained to recognize coarse-grained entities, and less
attention is given to classifying entities into a hierarchy of fine-grained lower-level subtypes …

A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers

K Huang, F Mo, H Li, Y Li, Y Zhang, W Yi, Y Mao… - arXiv preprint arXiv …, 2024 - arxiv.org
The rapid development of Large Language Models (LLMs) demonstrates remarkable
multilingual capabilities in natural language processing, attracting global attention in both …

Multiconer v2: a large multilingual dataset for fine-grained and noisy named entity recognition

B Fetahu, Z Chen, S Kar, O Rokhlenko… - arXiv preprint arXiv …, 2023 - arxiv.org
We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering
33 entity classes across 12 languages, in both monolingual and multilingual settings. This …

CNER: Concept and Named Entity Recognition

G Martinelli, F Molfese, S Tedeschi… - Proceedings of the …, 2024 - aclanthology.org
Named entities–typically expressed via proper nouns–play a key role in Natural Language
Processing, as their identification and comprehension are crucial in tasks such as Relation …

DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition

Z Tan, S Huang, Z Jia, J Cai, Y Li, W Lu… - arXiv preprint arXiv …, 2023 - arxiv.org
The MultiCoNER\RNum {2} shared task aims to tackle multilingual named entity recognition
(NER) in fine-grained and noisy scenarios, and it inherits the semantic ambiguity and low …

Entity recognition from colloquial text

T Babaian, J Xu - Decision Support Systems, 2024 - Elsevier
Extraction of concepts and entities of interest from non-formal texts such as social media
posts and informal communication is an important capability for decision support systems in …

NLPeople at SemEval-2023 task 2: A staged approach for multilingual named entity recognition

M El-karef, N Herr, S Tanaka… - Proceedings of the 17th …, 2023 - aclanthology.org
The MultiCoNER II shared task aims at detecting complex, ambiguous named entities with
fine-grained types in a low context setting. Previous winning systems incorporated external …

Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations

S Peng, Z Sun, S Loftus, B Plank - arXiv preprint arXiv:2402.01423, 2024 - arxiv.org
Named Entity Recognition (NER) is a key information extraction task with a long-standing
tradition. While recent studies address and aim to correct annotation errors via re-labeling …