Conceptual Modeling Meets Domain Ontology Development

Storey, Veda C.

doi:10.4018/jdm.2017010102

Cited by 13 publications

(1 citation statement)

References 48 publications

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“…Topic event extraction can be divided into ontology-based topic event extraction (Lee et al, 2003;Chua et al, 2012;Wu et al, 2020) and event frame-based topic event extraction (Wand & Weber, 2017;Storey, 2017;Wu et al, 2020).…”

Section: Topic Event Extractionmentioning

confidence: 99%

Semi-Supervised Event Extraction Incorporated With Topic Event Frame

Miao

et al. 2023

Journal of Database Management

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Supervised Meta-event extraction suffers from two limitations: (1) The extracted meta-events only contain local semantic information and do not present the core content of the text; (2) model performance is easily degraded because of labeled samples with insufficient number and poor quality. To overcome these limitations, this study presents an approach called frame-incorporated semi-supervised topic event extraction (FISTEE), which aims to extract topic events containing global semantic information. Inspired by the frame-based knowledge representation, a topic event frame is developed to integrate multiple meta-events into a topic event. Combined with the tri-training algorithm, a strategy for selecting unlabeled samples is designed to expand the training sets, and labeling models based on conditional random field (CRF) are constructed to label meta-events. The experimental results show that the event extraction performance of FISTEE is better than supervised learning-based approaches. Furthermore, the extracted topic events can present the core content of the text.

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