ISBN: 978-981-11-0008-6 DOI: 10.18178/wcse.2016.06.099
Automated Essay Scoring by Combining Syntactically Enhanced Latent Semantic Analysis and Coreference Resolution
Abstract— The objective of this research is to measure how much syntactic information (in the form of
word order) and Coreference Resolution affect the result of Automated Essay Scoring (AES) using Latent
Semantic Analysis (LSA). To incorporate the syntactic information, Syntactically Enhanced LSA (SELSA) is
used, whilst Stanford CoreNLP Natural Language Processing Toolkit is used for the Coreference Resolution.
To evaluate the results, we calculate the average absolute difference between the system score and human
score for each essay. Based on the results, we can conclude that syntactic information, when combined with
Coreference Resolution, do not have higher correlation to human score than LSA (an average absolute
difference of 0.15748 as opposed to LSA’s 0.12597). But interestingly, the two techniques work better when
they are used together, rather than when they are used separately. We also develop a new algorithm to
calculate the scores, with a better average absolute difference, which is as high as 0.08969.
Index Terms— automated essay scoring, syntactically enhanced latent semantic analysis, word order, coreference resolution.
Gilbert Wonowidjojo, Michael S. Hartono, Frendy, Derwin Suhartono
Bina Nusantara University, Computer Science Department, INDONESIA
Almodad B. Asmani
Bina Nusantara University, English Department, INDONESIA
Cite: Gilbert Wonowidjojo, Michael S. Hartono, Frendy, Derwin Suhartono, Almodad B. Asmani, "Automated Essay Scoring by Combining Syntactically Enhanced Latent Semantic Analysis and Coreference Resolution," Proceedings of 2016 6th International Workshop on Computer Science and Engineering, pp. 580-584, Tokyo, 17-19 June, 2016.