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Method and system for text classification based on learning of transferable feature representations from a source domain
Method and system for text classification based on learning of transferable feature representations from a source domain
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机译：基于从源域学习可转移特征表示的文本分类方法和系统
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摘要
The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving real-time input data comprising labeled instances of the source domain and unlabeled instances of the target domain from a computing device. The method further includes determining source specific representation corresponding to the source domain and a common representation shared between the source domain and the target domain. Based on a positive contribution from the source specific representation and the common representation, the labeled instances of the source domain are classified. The method further includes training a generalized classifier based on a positive contribution from the common representation. The method further includes automatically performing text classification on the unlabeled instances of the target domain based on the trained generalized classifier. The result of the text classification is rendered at a user interface of the computing device.
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