We investigate the statistical filtering of phishing emails, where a classifier is trained on characteristic features of existing emails and subsequently is able to identify new phishing emails with different contents. We propose advanced email features generated by adaptively trained Dynamic Markov Chains and by novel latent Class-Topic Models. On a publicly available test corpus classifiers using these features are able to reduce the number of misclassified emails by two thirds compared to previous work. Using a recently proposed more expressive evaluation method we show that these results are statistically significant. In addition we successfully tested our approach on a non-public email corpus with a real-life composition.