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- W4317035032 abstract "In this paper, we evaluate the Lbl2Vec approach for unsupervised text document classification. Lbl2Vec requires only a small number of keywords describing the respective classes to create semantic label representations. For classification, Lbl2Vec uses cosine similarities between label and document representations, but no annotation information. We show that Lbl2Vec significantly outperforms common unsupervised text classification approaches and a widely used zero-shot text classification approach. Furthermore, we show that using more precise keywords can significantly improve the classification results of similarity-based text classification approaches." @default.
- W4317035032 created "2023-01-18" @default.
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- W4317035032 date "2023-01-01" @default.
- W4317035032 modified "2023-09-27" @default.
- W4317035032 title "Semantic Label Representations with Lbl2Vec: A Similarity-Based Approach for Unsupervised Text Classification" @default.
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- W4317035032 doi "https://doi.org/10.1007/978-3-031-24197-0_4" @default.
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