Another recent approach is described by<cite> Mohler et al. (2011)</cite> , hereafter referred to as the Texas system. Student responses and target responses are annotated using a dependency parser. Thereupon, subgraphs of the dependency structures are constructed in order to map one response to the other. These alignments are generated using machine learning. Dealing with subgraphs allows for variation in word order between the two responses that are to be compared. In order to account for meaning, they combine lexical semantic similarity with the aforementioned alignment. They make use of several WordNet-based measures and two corpus-based measures, namely Latent Semantic Analysis and Explicit Semantic Analysis (ESA, Gabrilovich and Markovitch 2007) .