Graph Representation Learning in Document Wikification

Saeidi, Mozhgan; Milios, Evangelos; Zeh, Norbert

doi:10.1007/978-3-030-86159-9_37

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…This generally involves defining a similarity measure between a mention and a candidate entity, as well as some criterion that promotes the coherence of entities across multiple mentions (e.g., to ensure that the mention "Tesla" is not linked to the inventor if the rest of the document is about the car manufacturer). While older approaches generally used ad hoc measures of "semantic relatedness" and the like [93], more recent work often uses deep neural models to obtain vector representations of mentions (including their context) and entities [94,95]. Alternatively, some studies emphasize simple and fast-to-compute methods suitable for very large-scale wikification.…”

Section: Computational Linguistics: Extracting Emotions and Smellsmentioning

confidence: 99%

Harvesting Context and Mining Emotions Related to Olfactory Cultural Heritage

Massri

Novalija

Mladenić

et al. 2022

MTI

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This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, particularly to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel smell tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with the emotional charge of situations. The experimental results show that we can detect smells and emotions in fairy tales with an F1 score of 91.62 and 79.2, respectively.

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Section: Computational Linguistics: Extracting Emotions and Smellsmentioning

confidence: 99%