We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately graded than if the semantic measures were used in isolation.

In the final stage (Section 3.4), we produce an overall grade based upon the alignment scores found in the previous stage as well as the results of several semantic BOW similarity measures (Section 3.3).

All eight WordNet-based similarity measures listed in Section 3.3 plus the LSA model are used to produce these features.

In order to address this, we combine the graph alignment scores, which encode syntactic knowledge, with the scores obtained from semantic similarity measures .

One surprise while building this system was the consistency with which the novel technique of question demoting improved scores for the BOW similarity measures .

We use several string similarity measures as features, including SoftTFIDF (Cohen et al., 2003), Level 2 JaroWinkler (Cohen et al., 2003), Fellegi-Sunter (Fellegi and Sunter, 1969), and Monge-Elkan (Monge and Elkan, 1996).

Like other approaches (Basu et al., 2004; Xing et al., 2003; Klein et al., 2002), we learn a similarity measure for clustering based on a set of must-link and cannot-link constraints.

Unlike prior work, our algorithm exploits multiple views of the data to improve the similarity measure .