[BibTeX] [RIS]
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.
Authors Sappadla, Prateek Veeranna
Nam, Jinseok
Loza Mencía, Eneldo
Fürnkranz, Johannes