The paraphrase identification task has practical importance in the NLP community because of the need to deal with the pervasive problem of linguistic variation. Accurate methods should help improve the performance of NLP applications, including machine translation, information retrieval, question answering, text summarization, document clustering and plagiarism detection, amongst others. We ... [Show full abstract] consider an approach to paraphrase identification that may be considered “knowledge-lean”. Our approach minimizes the need for data transformation and avoids the use of knowledge-based tools and resources. Candidate paraphrase pairs are represented using combinations of word- and character-based features. We show that SVM classifiers may be trained to distinguish paraphrase and non-paraphrase pairs across a number of different paraphrase corpora with good results. Analysis shows that features derived from character bigrams are particularly informative. We also describe recent experiments in identifying paraphrase for Russian, a language with rich morphology and free word order that presents a particularly interesting challenge for our knowledge-lean approach. We are able to report good results on a three-way paraphrase classification task.