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|Abstract=Frequently, Text Classification is limited by insufficient training data. This problem is addressed by Zero-Shot Classification through the inclusion of external class definitions and then exploiting the relations between classes seen during training and unseen classes (Zero-shot). However, it requires a class embedding space capable of accurately representing the semantic relatedness between classes. This work defines an intrinsic evaluation based on greater-than constraints to provide a better understanding of this relatedness. The results imply that textual embeddings are able to capture more semantics than Knowledge Graph embeddings, but combining both modalities yields the best performance. | |Abstract=Frequently, Text Classification is limited by insufficient training data. This problem is addressed by Zero-Shot Classification through the inclusion of external class definitions and then exploiting the relations between classes seen during training and unseen classes (Zero-shot). However, it requires a class embedding space capable of accurately representing the semantic relatedness between classes. This work defines an intrinsic evaluation based on greater-than constraints to provide a better understanding of this relatedness. The results imply that textual embeddings are able to capture more semantics than Knowledge Graph embeddings, but combining both modalities yields the best performance. | ||
| − | |Download= | + | |Download=paper8.pdf |
| + | |Link=https://ceur-ws.org/Vol-3034/paper8.pdf | ||
|Forschungsgruppe=Information Service Engineering | |Forschungsgruppe=Information Service Engineering | ||
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Version vom 10. November 2022, 13:24 Uhr
Understanding Class Representations: An Intrinsic Evaluation of Zero-Shot Text Classification
Understanding Class Representations: An Intrinsic Evaluation of Zero-Shot Text Classification
Published: 2021
Oktober
Buchtitel: Proceedings of DL4KG workshop, co-located with the 20th International Semantic Web Conference (ISWC 2021)
Verlag: CEUR Workshop Proceedings
Referierte Veröffentlichung
BibTeX
Kurzfassung
Frequently, Text Classification is limited by insufficient training data. This problem is addressed by Zero-Shot Classification through the inclusion of external class definitions and then exploiting the relations between classes seen during training and unseen classes (Zero-shot). However, it requires a class embedding space capable of accurately representing the semantic relatedness between classes. This work defines an intrinsic evaluation based on greater-than constraints to provide a better understanding of this relatedness. The results imply that textual embeddings are able to capture more semantics than Knowledge Graph embeddings, but combining both modalities yields the best performance.
Download: Media:paper8.pdf
Weitere Informationen unter: Link
Information Service Engineering