  Latent Semantic Analysis (LSA) is a widely used Information Retrieval method
based on "bag-of-words" assumption. However, according to general conception,
syntax plays a role in representing meaning of sentences. Thus, enhancing LSA
with part-of-speech (POS) information to capture the context of word
occurrences appears to be theoretically feasible extension. The approach is
tested empirically on a automatic essay grading system using LSA for document
similarity comparisons. A comparison on several POS-enhanced LSA models is
reported. Our findings show that the addition of contextual information in the
form of POS tags can raise the accuracy of the LSA-based scoring models up to
10.77 per cent.
