Improved relation extraction through key phrase identification using community detection on dependency trees

• Published in: Computer speech & language Vol. 89; p. 101706
• Main Authors: Liu, Shuang, Chen, Xunqin, Meng, Jiana, Lukač, Niko
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2025
• Subjects: Community detection algorithms; Dependency trees; Relation extraction; Relation extraction; Community detection algorithms; Dependency trees
• ISSN: 0885-2308
• DOI: 10.1016/j.csl.2024.101706

•Discovering phrases in text by using community detection algorithms on syntax dependency trees.•Enhancing entity and sentence representations with a hierarchical attention mechanism.•The importance of key phrase recognition in relation extraction tasks is proved by different feature extraction methods. A method for extracting relations from sentences by utilizing their dependency trees to identify key phrases is presented in this paper. Dependency trees are commonly used in natural language processing to represent the grammatical structure of a sentence, and this approach builds upon this representation to extract meaningful relations between phrases. Identifying key phrases is crucial in relation extraction as they often indicate the entities and actions involved in a relation. The method uses community detection algorithms on the dependency tree to identify groups of related words that form key phrases, such as subject-verb-object structures. The experiments on the Semeval-2010 task8 dataset and the TACRED dataset demonstrate that the proposed method outperforms existing baseline methods.