Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
The Debate on Automated Essay Grading
IEEE Intelligent Systems
Approximate Natural Language Understanding for an Intelligent Tutor
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
On an equivalence between PLSI and LDA
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Benefits of modularity in an automated essay scoring system
Proceedings of the COLING-2000 Workshop on Using Toolsets and Architectures To Build NLP Systems
Predicting strong associations on the basis of corpus data
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
The Knowledge Engineering Review
The automatic identification of lexical variation between language varieties
Natural Language Engineering
Applying latent dirichlet allocation to automatic essay grading
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Measuring the use of factual information in test-taker essays
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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Probabilistic Latent Semantic Analysis (PLSA) is an information retrieval technique proposed to improve the problems found in Latent Semantic Analysis (LSA). We have applied both LSA and PLSA in our system for grading essays written in Finnish, called Automatic Essay Assessor (AEA). We report the results comparing PLSA and LSA with three essay sets from various subjects. The methods were found to be almost equal in the accuracy measured by Spearman correlation between the grades given by the system and a human. Furthermore, we propose methods for improving the usage of PLSA in essay grading.