%Aigaion2 BibTeX export from Knowledge Engineering Publications
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     author = {Sappadla, Prateek Veeranna and Nam, Jinseok and Loza Menc{\'{\i}}a, Eneldo and F{\"{u}}rnkranz, Johannes},
      month = apr,
      title = {Using Semantic Similarity for Multi-Label Zero-Shot Classification of Text Documents},
  booktitle = {Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN-16)},
       year = {2016},
  publisher = {d-side publications},
    address = {Bruges, Belgium},
        url = {https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-174.pdf},
   abstract = {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 embed-
ding 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 em-
bedding. Experiments on three textual datasets across various domains show that
even such a simple technique yields considerable performance improvements over
a simple uninformed baseline.}