activity
20212023
most citedText2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction

14 citations · 57 across the 15 of their papers we have counts for

collaborators

15 papers

cs.CL20225 cited

Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction

Tianshu Wang, Hongyu Lin, Cheng Fu +6

Entity matching (EM) is the most critical step for entity resolution (ER). While current deep learningbased methods achieve very impressive performance on standard EM benchmarks, t…

cs.CL20229 cited

Unified Structure Generation for Universal Information Extraction

Yaojie Lu, Qing Liu, Dai Dai +5

Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. In this paper, we propose a unified text-to-structure generation fra…

cs.CL2022

Pre-training to Match for Unified Low-shot Relation Extraction

Fangchao Liu, Hongyu Lin, Xianpei Han +2

Low-shot relation extraction~(RE) aims to recognize novel relations with very few or even no samples, which is critical in real scenario application. Few-shot and zero-shot RE are…

cs.CL2022

ECO v1: Towards Event-Centric Opinion Mining

Ruoxi Xu, Hongyu Lin, Meng Liao +5

Events are considered as the fundamental building blocks of the world. Mining event-centric opinions can benefit decision making, people communication, and social good. Unfortunate…

cs.CL2022

Can Prompt Probe Pretrained Language Models? Understanding the Invisible Risks from a Causal View

Boxi Cao, Hongyu Lin, Xianpei Han +2

Prompt-based probing has been widely used in evaluating the abilities of pretrained language models (PLMs). Unfortunately, recent studies have discovered such an evaluation may be…

cs.CL2022

Few-shot Named Entity Recognition with Self-describing Networks

Jiawei Chen, Qing Liu, Hongyu Lin +2

Few-shot NER needs to effectively capture information from limited instances and transfer useful knowledge from external resources. In this paper, we propose a self-describing mech…