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id: 36982

Using semantic similarity for multi-label zero-shot classification of text documents Veeranna, Sappadla Prateek; Nam, Jinseok; Mencía, Eneldo Loza; Fürnkranz, Johannes Sammelbandbeitrag | Aus: European Symposium on Artificial Neural Networks (Hrsg.): ESANN 2016 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium), 27-29 April 2016 | Bruges: European Symposium on Artificial Neural Networks | 2016 36982 Endnote: Autor*innen: Veeranna, Sappadla Prateek; Nam, Jinseok; Mencía, Eneldo Loza; Fürnkranz, Johannes
Titel: Using semantic similarity for multi-label zero-shot classification of text documents
Aus: European Symposium on Artificial Neural Networks (Hrsg.): ESANN 2016 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium), 27-29 April 2016, Bruges: European Symposium on Artificial Neural Networks, 2016 , S. 423-428
URL: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-174.pdf
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Computerlinguistik; Klassifikation; Semantik; Text
Abstract (english): In this paper, we examine a simple approach to zero-shot multi-label text classification, i.e., to the problem of predicting multiple, possibly previously unseen labels for a document. In particular, we propose to use a semantic embedding of label and document words and base the prediction of previously unseen labels on the similarity between the label name and the document words in this embedding. Experiments on three textual datasets across various domains show that even such a simple technique yields considerable performance improvements over a simple uninformed baseline. (DIPF/Orig.)
DIPF-Abteilung: Informationszentrum Bildung