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- W4375846411 abstract "Text classification faces a significant challenge of insufficient training data, especially for long-tail class distributions and new, emerging classes with no specific training data. Therefore, a zero-shot setting becomes necessary to enable transfer learning by utilizing external knowledge that connects the external knowledge of previously unseen classes to texts. Existing zero-shot text classifiers rely solely on distributional semantics derived from large language models, which are based on class names or natural language descriptions. However, this implicit knowledge contains ambiguities, is unable to capture logical relationships, and is not an effective representation of factual knowledge. To overcome these limitations, the addition of explicit, external knowledge is proposed, with knowledge graphs being an ideal choice to provide domain-specific knowledge that is explicit, unambiguous, and complementary. Thus, this thesis aims to explore the use of graph-based knowledge as an additional modality for zero-shot text classification. It investigates the impact of including domain-specific knowledge on the ability to handle domain shifts, in addition to a general study of this modality." @default.
- W4375846411 created "2023-05-10" @default.
- W4375846411 creator A5082241433 @default.
- W4375846411 date "2023-05-07" @default.
- W4375846411 modified "2023-09-28" @default.
- W4375846411 title "Enhancing Text Classification in Zero-Shot Settings using Knowledge Representations based on Graphs." @default.
- W4375846411 doi "https://doi.org/10.31219/osf.io/m4eqb" @default.
- W4375846411 hasPublicationYear "2023" @default.
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