C-ICL: Contrastive In-context Learning for Information Extraction

no code implementations • 17 Feb 2024 • Ying Mo, Jian Yang, Jiahao Liu, Shun Zhang, Jingang Wang, Zhoujun Li

Recently, there has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related to named entity recognition (NER) and relation extraction (RE).

In-Context Learning Miscellaneous +4

mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning

no code implementations • 17 Aug 2023 • Ying Mo, Jian Yang, Jiahao Liu, Qifan Wang, Ruoyu Chen, Jingang Wang, Zhoujun Li

A multi-view contrastive learning framework is introduced to encompass semantic contrasts between source, codeswitched, and target sentences, as well as contrasts among token-to-token relations.

Contrastive Learning named-entity-recognition +2

Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition

no code implementations • 20 Mar 2023 • Ying Mo, Hongyin Tang, Jiahao Liu, Qifan Wang, Zenglin Xu, Jingang Wang, Wei Wu, Zhoujun Li

There are three types of NER tasks, including flat, nested and discontinuous entity recognition.

Boundary Detection named-entity-recognition +4

Multilingual Entity and Relation Extraction from Unified to Language-specific Training

no code implementations • 11 Jan 2023 • Zixiang Wang, Jian Yang, Tongliang Li, Jiaheng Liu, Ying Mo, Jiaqi Bai, Longtao He, Zhoujun Li

In this paper, we propose a two-stage multilingual training method and a joint model called Multilingual Entity and Relation Extraction framework (mERE) to mitigate language interference across languages.

Relation Relation Extraction +1