Semeval-2022 task 11: Multilingual complex named entity recognition (multiconer)

S Malmasi, A Fang, B Fetahu, S Kar… - Proceedings of the …, 2022 - aclanthology.org
We present the findings of SemEval-2022 Task 11 on Multilingual Complex Named Entity
Recognition MULTICONER. Divided into 13 tracks, the task focused on methods to identify …

DAE-NER: Dual-channel attention enhancement for Chinese named entity recognition

J Liu, M Sun, W Zhang, G Xie, Y Jing, X Li… - Computer Speech & …, 2024 - Elsevier
Abstract Named Entity Recognition (NER) is an important component of Natural Language
Processing (NLP) and is a fundamental yet challenging task in text analysis. Recently, NER …

Deep learning for named entity recognition: a survey

Z Hu, W Hou, X Liu - Neural Computing and Applications, 2024 - Springer
Named entity recognition (NER) aims to identify the required entities and their types from
unstructured text, which can be utilized for the construction of knowledge graphs. Traditional …

Multilingual large language model: A survey of resources, taxonomy and frontiers

L Qin, Q Chen, Y Zhou, Z Chen, Y Li, L Liao… - arXiv preprint arXiv …, 2024 - arxiv.org
Multilingual Large Language Models are capable of using powerful Large Language
Models to handle and respond to queries in multiple languages, which achieves remarkable …

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 …

Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMs

X Zhang, S Li, B Hauer, N Shi, G Kondrak - arXiv preprint arXiv …, 2023 - arxiv.org
Large Language Models (LLMs) have demonstrated exceptional natural language
understanding abilities and have excelled in a variety of natural language processing (NLP) …

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 …

MaChAmp at SemEval-2023 tasks 2, 3, 4, 5, 7, 8, 9, 10, 11, and 12: On the Effectiveness of Intermediate Training on an Uncurated Collection of Datasets.

R Van Der Goot - Proceedings of the 17th International Workshop …, 2023 - aclanthology.org
To improve the ability of language models to handle Natural Language Processing (NLP)
tasks and intermediate step of pre-training has recently beenintroduced. In this setup, one …

USTC-NELSLIP at SemEval-2022 task 11: Gazetteer-adapted integration network for multilingual complex named entity recognition

B Chen, JY Ma, J Qi, W Guo, ZH Ling, Q Liu - arXiv preprint arXiv …, 2022 - arxiv.org
This paper describes the system developed by the USTC-NELSLIP team for SemEval-2022
Task 11 Multilingual Complex Named Entity Recognition (MultiCoNER). We propose a …

NEREL: a Russian information extraction dataset with rich annotation for nested entities, relations, and wikidata entity links

N Loukachevitch, E Artemova, T Batura… - Language Resources …, 2024 - Springer
This paper describes NEREL—a Russian news dataset suited for three tasks: nested named
entity recognition, relation extraction, and entity linking. Compared to flat entities, nested …