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Structural correspondence learning (SCL) is an effective method forcross-lingual sentiment classification. This approach uses unlabeled documentsalong with a word translation oracle to automatically induce task specific,cross-lingual correspondences. It transfers knowledge through identifyingimportant features, i.e., pivot features. For simplicity, however, it assumesthat the word translation oracle maps each pivot feature in source language toexactly only one word in target language. This one-to-one mapping between wordsin different languages is too strict. Also the context is not considered atall. In this paper, we propose a cross-lingual SCL based on distributedrepresentation of words; it can learn meaningful one-to-many mappings for pivotwords using large amounts of monolingual data and a small dictionary. Weconduct experiments on NLP\&CC 2013 cross-lingual sentiment analysis dataset,employing English as source language, and Chinese as target language. Ourmethod does not rely on the parallel corpora and the experimental results showthat our approach is more competitive than the state-of-the-art methods incross-lingual sentiment classification.

Structural Correspondence Learning for Cross-lingual Sentiment Classification with One-to-many Mappings