Latent semantic analysis (LSA) has been used in several intelligent tutoring systems(ITS's) for assessing students' learning by evaluating their answers to questions in the tutoring domain. It is based on word-document cooccurrence statistics in the training corpus and a dimensionality reduction technique. However, it doesn't consider the word-order or syntactic information, which can improve the knowledge representation and therefore lead to better performance of an ITS. We present here an approach called Syntactically Enhanced LSA (SELSA) which generalizes LSA by considering a word along with its syntactic neighborhood given by the part-of-speech tag of its preceding word, as a unit of knowledge representation. The experimental results on AutoTutor task to evaluate students' answers to basic computer science questions by SELSA and its comparison with LSA are presented in terms of several cognitive measures. SELSA is able to correctly evaluate a few more answers than LSA but is having less correlation with human evaluators than LSA has. It also provides better discrimination of syntactic-semantic knowledge representation than LSA.
REF proposed a similar model called Syntactically Enhanced LSA by considering a word along with its syntactic neighborhood (obtained from the part-of-speech tag of its preceding word).
Automatic Evaluation Of Students' Answers Using Syntactically Enhanced LSA
Workshop On Building Educational Applications Using Natural Language Processing
Computer Science