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

本文提出了一种基于分布式词表示的跨语言结构对应学习方法，能够在没有平行语料库的情况下学习具有意义的一对多映射，用于处理跨语言情感分类问题，在 NLP&CC 2013 跨语言情感分析数据集上进行试验，证明该方法比最先进的方法更具竞争力。

跨语言情感分类的结构对应学习及一对多映射

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

Cross-lingual adaptation, a special case of domain adaptation, refers to the
transfer of classification knowledge between two languages. In this article we
describe an extension of Structural Correspondence Learning (SCL), a recently
proposed algorithm for domain adaptation, for cross-lingual adaptation. The
proposed method uses unlabeled documents from both languages, along with a word
translation oracle, to induce cross-lingual feature correspondences. From these
correspondences a cross-lingual representation is created that enables the
transfer of classification knowledge from the source to the target language.
The main advantages of this approach over other approaches are its resource
efficiency and task specificity.
We conduct experiments in the area of cross-language topic and sentiment
classification involving English as source language and German, French, and
Japanese as target languages. The results show a significant improvement of the
proposed method over a machine translation baseline, reducing the relative
error due to cross-lingual adaptation by an average of 30% (topic
classification) and 59% (sentiment classification). We further report on
empirical analyses that reveal insights into the use of unlabeled data, the
sensitivity with respect to important hyperparameters, and the nature of the
induced cross-lingual correspondences.

通过使用未标记的文档和单词翻译神器，利用结构对应学习方法进行跨语言适应，实现从一种语言到另一种语言的分类知识传递，相对于其它方法，该方法具有资源高效性和任务特异性，并在英语为源语言、德、法、日语为目标语言的跨语言主题和情感分类实验中展现出比机器翻译基线方法平均减少 30%（主题分类）和 59%（情感分类）的较小的相对误差。