Penn: Using Word Similarities to better Estimate Sentence Similarity.

We present the Penn system for SemEval-2012 Task 6, computing the degree of semantic equivalence between two sentences. We explore the contributions of different vector models for computing sentence and word similarity: Collobert and Weston embeddings as well as two novel approaches, namely eigen-words and selectors . These embeddings provide different measures of distributional similarity between words, and their contexts. We used regression to combine the different similarity measures, and found that each provides partially independent predictive signal above baseline models.