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Evaluating Vector Space Models with Canonical Correlation Analysis

Sami Virpioja, Mari-Sanna Paukkeri, Abhishek Tripathi, Tiina Lindh-Knuutila, Krista Lagus
Vector space models are used in language processing applications for calculating semantic similarities of words or documents. The vector spaces are generated with feature extraction methods for text data. However, evaluation of the feature extraction methods may be difficult. Indirect evaluation in an application is often time-consuming and the results may not generalize to other applications, whereas direct evaluations that measure the amount of captured semantic information usually require human evaluators or annotated data sets. We propose a novel direct evaluation method based on canonical correlation analysis (CCA), the classical method for finding linear relationship between two data sets. In our setting, the two sets are parallel text documents in two languages. A good feature extraction method should provide representations that reflect the semantic content of the documents. Assuming that the underlying semantic content is independent of the language, we can study which feature extraction methods capture it best by measuring the dependence between the representations of the same documents in two languages. In the case of CCA, the applied measure of dependence is correlation. The evaluation method is based on unsupervised learning, it is language and domain independent, and it does not require additional resources besides a parallel corpus. We demonstrate the evaluation method on a sentence-aligned parallel corpus. The method is validated by showing that the obtained results with bag-of-words representations are intuitive and agree well with the previous findings. Moreover, we examine the performance of the proposed evaluation method with indirect evaluation methods in simple sentence matching tasks, and a quantitative manual evaluation of word translations. The results of the proposed method correlate well with the results of the indirect and manual evaluations.
Natural Language Engineering Journal, Editor : Cambridge University Press 2011.