An Effective System for Multi-format Information Extraction

Liu, Yaduo; Zhang, Longhui; Yin, Shujuan; Zhao, Xiaofeng; Ren, Feng

doi:10.1007/978-3-030-88483-3_37

Cited by 1 publication

(1 citation statement)

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“…This improves the generalization performance and removes noise from our training data. Lexical applications such as guiding words [14,15], robustness [16,17], and low data resources [18,19] have inspired similar approaches of this type.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Role of Self-Adaptive Feature Words in Relation Quintuple Extraction for Scientific Literature

Liu,

Fu,

Xia

et al. 2024

Applied Sciences

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Extracting relation quintuple and feature words from unstructured text is a prelude to the construction of the scientific knowledge base. At present, the prior works use explicit clues between entities to study this task but ignore the use and the association of the feature words. In this work, we propose a new method to generate self-adaptive feature words from the original text for every single sample. These words can add additional correlation information to the knowledge graph. We allow the model to generate a new word representation and apply it to the original sentence to judge the relation type and locate the head and tail of the relation quintuple. Compared with the previous works, the feature words increase the flexibility of relying on information and improve the explanatory ability. Extensive experiments on scientific field datasets illustrate that the self-adaptive feature words method (SAFW) is good at ferreting out the unique feature words and obtaining the core part for the quintuple. It achieves good performance on four public datasets and obtains a markable performance improvement compared with other baselines.

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