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- W1551945554 abstract "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." @default.
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- W1551945554 date "2006-10-19" @default.
- W1551945554 modified "2023-09-27" @default.
- W1551945554 title "Applying Part-of-Seech Enhanced LSA to Automatic Essay Grading" @default.
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