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• W2623936677 abstract "Abstract Word embeddings have received a lot of attention in the natural language processing area for their capabilities of capturing inner words semantics (e.g. word2vec, GloVe). The need of catching semantics at a higher and more abstract level led to creation of models like paragraph vectors for sentences and documents, seq2vec for biological sequences. In this paper we illustrate an approach for creating vector representations for hierarchical content where each node in the hierarchy is represented as a (recursive) function of its paragraph vector and the hierarchical vectors of its child nodes, computed via matrix factorization. We evaluate the effectiveness of our solution against flat paragraph vectors on a text categorization task obtaining significant µF1 improvements." @default.
• W2623936677 created "2017-06-15" @default.
• W2623936677 creator A5083417786 @default.
• W2623936677 date "2017-01-01" @default.
• W2623936677 modified "2023-09-23" @default.
• W2623936677 title "par2hier: towards vector representations for hierarchical content" @default.
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• W2623936677 doi "https://doi.org/10.1016/j.procs.2017.05.077" @default.
• W2623936677 hasPublicationYear "2017" @default.
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