Improving SCL Model for Sentiment-Transfer Learning
نویسندگان
چکیده
In recent years, Structural Correspondence Learning (SCL) is becoming one of the most promising techniques for sentiment-transfer learning. However, SCL model treats each feature as well as each instance by an equivalent-weight strategy. To address the two issues effectively, we proposed a weighted SCL model (W-SCL), which weights the features as well as the instances. More specifically, W-SCL assigns a smaller weight to high-frequency domain-specific (HFDS) features and assigns a larger weight to instances with the same label as the involved pivot feature. The experimental results indicate that proposed W-SCL model could overcome the adverse influence of HFDS features, and leverage knowledge from labels of instances and pivot features.
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