Using Semantic Similarity for Multi-Label Zero-Shot Classification of Text Documents
| Type of publication: | Inproceedings |
| Citation: | esann16zeroshot |
| Booktitle: | Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN-16) |
| Year: | 2016 |
| Month: | April |
| 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. |
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