Cross-Lingual Knowledge Transferring by Structural Correspondence and Space Transfer.

Area of Science:

• Natural Language Processing
• Machine Learning
• Computational Linguistics

Background:

• Cross-lingual sentiment analysis (CLSA) faces challenges in resource-scarce languages due to limited labeled data.
• Transfer learning offers a promising solution by leveraging sentiment knowledge from resource-rich languages.
• Existing methods often struggle with knowledge loss during cross-lingual transfer.

Purpose of the Study:

• To propose a novel semisupervised transfer learning approach for CLSA.
• To enhance knowledge transfer efficiency and classification accuracy in target languages.
• To address the limitations of existing CLSA methods, particularly in low-resource scenarios.

Main Methods:

• Introduced semisupervised learning with Structural Correspondence Learning (SCL) and space transfer (ssSCL-ST).
• Explored intrinsic sentiment knowledge within the target language domain.
• Implemented pivot set extension and space transfer mechanisms to optimize knowledge transfer.

Main Results:

• The proposed ssSCL-ST approach demonstrated superior performance compared to state-of-the-art methods.
• Achieved significant improvements in cross-lingual sentiment classification accuracy.
• Effectively transferred sentiment knowledge without relying on parallel corpora.

Conclusions:

• The ssSCL-ST method offers an effective solution for CLSA in resource-scarce settings.
• Semisupervised learning combined with SCL and space transfer enhances cross-lingual knowledge transfer.
• The approach successfully overcomes the limitations of traditional monolingual and transfer learning methods.