Applying Part-of-Seech Enhanced LSA to Automatic Essay Grading

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dc.creatorKakkonen, Tuomo
dc.creatorMyller, Niko
dc.creatorSutinen, Erkki
dc.date2006-10-19
dc.date.accessioned2026-07-07T07:27:53Z
dc.date.available2026-07-07T07:27:53Z
dc.descriptionLatent 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.
dc.identifierhttps://arxiv.org/abs/cs/0610118
dc.identifierhttp://arxiv.org/abs/cs/0610118
dc.identifierProceedings of the 4th IEEE International Conference on Information Technology: Research and Education (ITRE 2006). Tel Aviv, Israel, 2006
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/117538
dc.subjectInformation Retrieval
dc.subjectComputation and Language
dc.titleApplying Part-of-Seech Enhanced LSA to Automatic Essay Grading
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