Name
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. |
Year | Venue | Keywords |
|---|---|---|
2006 | Clinical Orthopaedics and Related Research | automatic essay grading,index terms — latent semantic analysis,part-of-speech tagging,part of speech,bag of words,information retrieval,indexing terms,latent semantic analysis |
Field | DocType | Volume |
Contextual information,Grading (education),Information retrieval,Computer science,Natural language processing,Artificial intelligence,Document similarity,Latent semantic analysis,Syntax | Journal | abs/cs/061 |
ISSN | Citations | PageRank |
Proceedings of the 4th IEEE International Conference on
Information Technology: Research and Education (ITRE 2006). Tel Aviv, Israel,
2006 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tuomo Kakkonen | 1 | 80 | 11.82 |
| Niko Myller | 2 | 296 | 24.67 |
| Erkki Sutinen | 3 | 1131 | 188.05 |