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- W4385963672 abstract "This paper presents a framework in which hierarchical softmax is used to create a global hierarchical classifier. The approach is applicable for any classification task where there is a natural hierarchy among classes. We show empirical results on four text classification datasets. In all datasets the hierarchical softmax improved on the regular softmax used in a flat classifier in terms of macro-F1 and macro-recall. In three out of four datasets hierarchical softmax achieved a higher micro-accuracy and macro-precision." @default.
- W4385963672 created "2023-08-18" @default.
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- W4385963672 date "2023-08-02" @default.
- W4385963672 modified "2023-09-26" @default.
- W4385963672 title "Global Hierarchical Neural Networks using Hierarchical Softmax" @default.
- W4385963672 doi "https://doi.org/10.48550/arxiv.2308.01210" @default.
- W4385963672 hasPublicationYear "2023" @default.
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