Name
Abstract | ||
|---|---|---|
We report experiments on automatic essay grading using Latent Dirichlet Allocation (LDA). LDA is a “bag-of-words” type of language modeling and dimension reduction method, reported to outperform other related methods, Latent Semantic Analysis (LSA) and Probabilistic Latent Semantic Analysis (PLSA) in Information Retrieval (IR) domain. We introduce LDA in detail and compare its strengths and weaknesses to LSA and PLSA. We also compare empirically the performance of LDA to LSA and PLSA. The experiments were run with three essay sets consisting in total of 283 essays from different domains. On contrary to the findings in IR, LDA achieved slightly worse results compared to LSA and PLSA in the experiments. We state the reasons for LSA and PLSA outperforming LDA and indicate further research directions. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11816508_13 | FinTAL |
Keywords | Field | DocType |
research direction,latent dirichlet allocation,probabilistic latent semantic analysis,automatic essay grading,information retrieval,language modeling,related method,dimension reduction method,different domain,latent semantic analysis,automatic essay,bag of words,language model,dimension reduction | Slightly worse,Latent Dirichlet allocation,Dimensionality reduction,Grading (education),Computer science,Natural language,Natural language processing,Artificial intelligence,Probabilistic latent semantic analysis,Latent semantic analysis,Language model | Conference |
Volume | ISSN | ISBN |
4139 | 0302-9743 | 3-540-37334-9 |
Citations | PageRank | References |
10 | 0.70 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tuomo Kakkonen | 1 | 80 | 11.82 |
| Niko Myller | 2 | 296 | 24.67 |
| Erkki Sutinen | 3 | 1131 | 188.05 |