Automatic essay grading with probabilistic latent semantic analysis
T. Kakkonen, N. Myller, J. Timonen, and E. Sutinen. EdAppsNLP 05: Proceedings of the second workshop on Building Educational Applications Using NLP, page 29--36. Morristown, NJ, USA, Association for Computational Linguistics, (2005)
Abstract
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.
Description
Automatic essay grading with probabilistic latent semantic analysis
%0 Conference Paper
%1 1609835
%A Kakkonen, Tuomo
%A Myller, Niko
%A Timonen, Jari
%A Sutinen, Erkki
%B EdAppsNLP 05: Proceedings of the second workshop on Building Educational Applications Using NLP
%C Morristown, NJ, USA
%D 2005
%I Association for Computational Linguistics
%K aea caa freetext lsa
%P 29--36
%T Automatic essay grading with probabilistic latent semantic analysis
%U http://portal.acm.org/citation.cfm?id=1609835
%X 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.
@inproceedings{1609835,
abstract = {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.},
added-at = {2010-05-19T14:44:42.000+0200},
address = {Morristown, NJ, USA},
author = {Kakkonen, Tuomo and Myller, Niko and Timonen, Jari and Sutinen, Erkki},
biburl = {https://www.bibsonomy.org/bibtex/2c57b0965f8f3831e7e04059baf767029/ifland},
booktitle = {EdAppsNLP 05: Proceedings of the second workshop on Building Educational Applications Using NLP},
description = {Automatic essay grading with probabilistic latent semantic analysis},
interhash = {0163c262f8139ef5001935a5767a2d1e},
intrahash = {c57b0965f8f3831e7e04059baf767029},
keywords = {aea caa freetext lsa},
location = {Ann Arbor, Michigan},
pages = {29--36},
publisher = {Association for Computational Linguistics},
timestamp = {2010-05-19T14:44:42.000+0200},
title = {Automatic essay grading with probabilistic latent semantic analysis},
url = {http://portal.acm.org/citation.cfm?id=1609835},
year = 2005
}