Abstract
Transfer learning is one of the popular methods for solving the problem that the models built on the source domain cannot be directly applied to the target domain in the cross-domain sentiment classification. This paper proposes a transfer learning method based on the multi-layer convolutional neural network (CNN). Interestingly, we construct a convolutional neural network model to extract features from the source domain and share the weights in the convolutional layer and the pooling layer between the source and target domain samples. Next, we fine-tune the weights in the last layer, named the fully connected layer, and transfer the models from the source domain to the target domain. Comparing with the classical transfer learning methods, the method proposed in this paper does not need to retrain the network for the target domain. The experimental evaluation of the cross-domain data set shows that the proposed method achieves a relatively good performance.
Highlights
As is well known, the performance of training and update on machine learning models depends on the labeled data, one can get huge amounts of data, but can rarely correct the manually labeled data
The baseline methods are based on supervised learning, such as support vector machine (SVM), Naive Bayes (NB) and LR, directly adapt the classifiers trained from a single source to the target domain, while the gold standard is a source domain classifier which is trained on the same domain as it is tested
In order to compare with our transfer learning method, we perform some machine learning methods that no target domain data are used
Summary
The performance of training and update on machine learning models depends on the labeled data, one can get huge amounts of data, but can rarely correct the manually labeled data. The labeling work may be time-consuming and expensive, which presents a great challenge to machine learning models To solve this problem, Yang et al propose a transfer learning method [1], the core idea of which is to find the similarity between the source and target domains and to transfer the model or the labeled data used in the source domain to those in the target domain in the view of similarity. The ability to transfer knowledge is inherent, for example, if you can play tennis, you can learn to play badminton Because these activities often have a very high degree of similarity, one can solve new problems based on the existing learning methods and modify them gradually
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