Toward Better Understanding Older Adults: A Biography Brief Timeline Extraction Approach

An, Ning; Gui, Fang; Jin, Liuqi; Hong, Minghui; Yang, Jiaoyun

doi:10.1080/10447318.2022.2077278

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…It also enables them to delve into specific events, understanding the event details and developmental processes. This structure furnishes caregivers with the necessary data support and decision-making foundation to devise personalized care plans for older adults [33].…”

Section: Problem Definitionmentioning

confidence: 99%

“…The time component is from the time element of the merged event, and the summary component is the event summary extracted from the most reliable event determined in Section 3.4.2. We adopted the approach proposed in paper [33] to implement event summary generation. Furthermore, it is worth noting that oft-mentioned life stories and infrequently mentioned life stories hold distinct meanings for an individual.…”

Section: Event Tree Constructionmentioning

confidence: 99%

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Structured Life Narratives: Building Life Story Hierarchies with Graph-Enhanced Event Feature Refinement

Gui,

Yang,

Tang

et al. 2024

Applied Sciences

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The life stories of older adults encapsulate an array of personal experiences that reflect their care needs. However, due to inherent fuzzy features, fragmented natures, repetition, and redundancies, the practical application of the life story approach poses challenges for caregivers in acquiring and comprehending these narratives. Addressing this challenge, our study introduces a novel approach called Life Story Hierarchies with Graph-Enhanced Event Feature Refinement (LSH-GEFR). LSH-GEFR constructs a bilayer graph. Firstly, the event element map leverages intricate relationships between event elements to extract environmental features, providing a detailed context for understanding each event element. Secondly, the event map explores the complex web of relationships between the events themselves, allowing LSH-GEFR to generate a comprehensive understanding of each event and enhance its representation. Subsequently, we conducted experiments on different datasets and found that, in comparison with four advanced event tree generation methods, the proposed LSH-GEFR method outperformed them in terms of path coherence, branch reasonableness, and overall readability when generating life story hierarchies. Over 84.91% of the structured life narratives achieved readability, marking a 5.96% increase over the best-performing approach at the baseline.

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Section: Problem Definitionmentioning

confidence: 99%

Section: Event Tree Constructionmentioning

confidence: 99%