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A Review and Outlook of the Latest Results on Document-level Information Extraction
- 1 School of Computer Information and Engineering, School of Henan University of Economics and Law, Zhengzhou, China
- 2 College of Computer Science and Technology, Hainan University, Haikou, China
- 3 School of Computer Information and Engineering, School of Henan University of Economics and Law, Zhengzhou, China
* Author to whom correspondence should be addressed.
Abstract
Relationship extraction is a key step in information extraction and aims to identify entities from text data and identify semantic links between them, which is the foundation of knowledge graph construction. Improving relationship extraction efficiency can effectively enhance the quality of information extraction, which has an impact on the construction of a knowledge graph and subsequent downstream tasks. The paper improves the efficiency and quality of relationship extraction by optimizing the model, choosing the way of relationship extraction and proposing new evaluation parameter criteria. The paper summarizes the current state-of-the-art research results, and briefly describes SIEF, an evaluation mechanism that focuses on important sentences, Coreference Aid, which captures the internal structure of entities as well as the external fine-grained information, and FILR, a framework that extracts global document information and performs multi-granular logical reasoning. Aggregate semi-automated data augmentation by integrating large model languages, remote supervision and construction of domain graphs integrating domain information of entities, cross-article entity-centered relationship extraction capable of capturing connections between different articles, and the introduction of mean-accuracy averaging capable of evaluating the comprehensibility of the model are presented. This paper presents a vision of new and better optimization algorithms by describing the latest approaches to relationship extraction optimization and finding that they all have their own limitations.
Keywords
Document-Level Relationship Extraction, Framework Optimization, Natural Language Processing, Model Evaluation, Cross-document Relation Extraction.
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Cite this article
Li,H.;Wei,L.;Wang,Z. (2024). A Review and Outlook of the Latest Results on Document-level Information Extraction. Applied and Computational Engineering,96,120-129.
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