most citedExploring the Feasibility of ChatGPT for Event Extraction

56 citations · 65 across the 9 of their papers we have counts for

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cs.CL2023

Improving Few-shot and Zero-shot Entity Linking with Coarse-to-Fine Lexicon-based Retriever

Shijue Huang, Bingbing Wang, Libo Qin +2

Few-shot and zero-shot entity linking focus on the tail and emerging entities, which are more challenging but closer to real-world scenarios. The mainstream method is the ''retriev…

cs.CL2023★ 1 cited

MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System

Libo Qin, Shijue Huang, Qiguang Chen +5

Multi-modal sarcasm detection has attracted much recent attention. Nevertheless, the existing benchmark (MMSD) has some shortcomings that hinder the development of reliable multi-m…

cs.CL2023★ 6 cited

A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers

Xiaoyan Zhao, Yang Deng, Min Yang +6

Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and inf…

cs.CL2023

A Diffusion Model for Event Skeleton Generation

Fangqi Zhu, Lin Zhang, Jun Gao +3

Event skeleton generation, aiming to induce an event schema skeleton graph with abstracted event nodes and their temporal relations from a set of event instance graphs, is a critic…

cs.CL2023

Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary

Xiaokang Liu, Jianquan Li, Jingjing Mu +3

Open intent classification, which aims to correctly classify the known intents into their corresponding classes while identifying the new unknown (open) intents, is an essential bu…

cs.CL2023★ 56 cited

Exploring the Feasibility of ChatGPT for Event Extraction

Jun Gao, Huan Zhao, Changlong Yu +1

Event extraction is a fundamental task in natural language processing that involves identifying and extracting information about events mentioned in text. However, it is a challeng…