INSTRUCTIE: A Comprehensive Bilingual Dataset for Instruction-based Information Extraction
The INSTRUCTIE dataset provides a comprehensive bilingual (Chinese and English) resource for training large language models to perform instruction-based information extraction tasks across diverse domains.
Constructing a Comprehensive Bilingual Information Extraction Corpus: IEPILE
IEPILE is a comprehensive bilingual (English and Chinese) information extraction instruction corpus containing approximately 0.32B tokens, constructed by collecting and cleaning 33 existing datasets and introducing a schema-based instruction generation strategy to address the limitations of existing IE datasets.
Extracting Nested Events by Recognizing Pivot Elements
The core message of this paper is that recognizing pivot elements, which simultaneously act as arguments of outer-nest events and as triggers of inner-nest events, is crucial for effectively extracting nested event structures.
IPED: An Implicit Perspective for Relational Triple Extraction based on Diffusion Model by Jianli Zhao, Changhao Xu, Bin Jiang
IPED proposes an innovative approach for relational triple extraction using an implicit perspective and denoising diffusion model, achieving state-of-the-art performance and efficiency.
Addressing Definition Bias in Information Extraction: Probing Experiments and Mitigation Framework
Mitigating definition bias in information extraction is crucial for improving model performance and alignment with annotations.
Span-Oriented Information Extraction: A Unifying Perspective
A unifying perspective on information extraction tasks, centered around spans in text.
Representation Learning for Weakly Supervised Relation Extraction
Improving relation extraction performance with text representation learning.
Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Correlations
Introducing the CARLG framework for improved event argument extraction by incorporating contextual clues and role correlations.
Schema-Driven Information Extraction from Heterogeneous Tables: A Comprehensive Study
Large language models can efficiently extract structured data from diverse tables using schema-driven information extraction.
Structured Entity Extraction Using Large Language Models: Challenges and Innovations
The author explores the challenges in structured entity extraction and introduces a novel approach using Large Language Models (LLMs) to enhance effectiveness and efficiency.