We compare and combine two methods to approach the second textual entailment challenge (RTE-2): a shallow method based mainly on word-overlap and a method based on logical inference, using first-order theorem proving and model building techniques. We use a machine learning technique to combine features of both methods. We submitted two runs, one using only the shallow features, yielding an accuracy of 61.6%, and one using features of both methods, performing with an accuracy score of 60.6%. These figures suggest that logical inference didn´t help much. Closer inspection of the results revealed that only for some of the subtasks logical inference played a significant role in performance. We try to explain the reason for these results.