DocumentCode
3300522
Title
Automated essay scoring using set of literary sememes
Author
Chang, Tao-Hsing ; TSAI, Pei-Yen ; Lee, Chia-Hoang ; TAM, Hak-Ping
Author_Institution
Dept. of Comput. Sci., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung
fYear
2008
fDate
19-22 Oct. 2008
Firstpage
1
Lastpage
5
Abstract
Automatic essay scoring system is a very important research tool for many educational studies. Many researches indicate that AES systems should be able to analyze semantic characteristics of an essay and include more such features to score essays. This paper makes an assumption: some concepts that can be regarded as literary concepts would only be utilized by skillful writers. However, it is a difficult task to extract literal concepts due to small size of training corpora. This work uses a semantic network tool to overcome the problem. The concepts in essays can be transformed into sememes using the tool and literary concepts are also transformed into literary sememes. This work introduces a method which makes use of the literary sememes in an essay to score the essay. Experimental results show that the accuracy of the proposed method for Chinese essays is comparable to those as achieved by several current English AES systems.
Keywords
educational administrative data processing; natural language processing; Chinese essays; automatic essay scoring system; educational studies; literary sememes; semantic characteristics analysis; semantic network tool; Artificial intelligence; Computer science; Computer science education; Costs; Educational institutions; Machine intelligence; Natural language processing; Natural languages; Psychometric testing; Writing; Chinese writings; Essay scoring; literary sememes; semantic analysis; writing grading;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Language Processing and Knowledge Engineering, 2008. NLP-KE '08. International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4244-4515-8
Electronic_ISBN
978-1-4244-2780-2
Type
conf
DOI
10.1109/NLPKE.2008.4906764
Filename
4906764
Link To Document