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Autor:
Veeranna, Sappadla Prateek; Nam, Jinseok; Mencía, Eneldo Loza; Fürnkranz, Johannes:

Titel:
Using semantic similarity for multi-label zero-shot classification of text documents

Quelle:
In: 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) , 423-428

URL des Volltextes:
https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-174.pdf

Sprache:
Englisch

Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Computerlinguistik, Klassifikation, Semantik, Text


Abstract(englisch):
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.)


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last modified Nov 11, 2016