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- W2916953003 abstract "Word embeddings are successfully employed in various Natural Language Processing tasks, but training them requires large amount of text, which is scarce for Turkish. In this work, we collected large amounts of articles from two news websites and tags within web pages are used as labels. Obtained corpora are tested with various document classification models. Embedding based models performed better than models with the traditional TF-IDF features. A neural network that simultaneously learns the word embeddings and document classification performed the best." @default.
- W2916953003 created "2019-03-02" @default.
- W2916953003 creator A5057031258 @default.
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- W2916953003 date "2018-07-01" @default.
- W2916953003 modified "2023-09-26" @default.
- W2916953003 title "Document classification of SuDer Turkish news corpora [SuDer Türkçe haber derlemlerinin doküman sınıflandırması]" @default.
- W2916953003 hasPublicationYear "2018" @default.
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