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Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
2019
Proceedings of the 2019 Conference of the North
Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is therefore difficult and only limited previous works tackled this problem. In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem. Four kinds of
doi:10.18653/v1/n19-1108
dblp:conf/naacl/ZhangLG19
fatcat:pz4oubvlabdnzbbaufwmpdiyr4