Enhanced Latent Semantic Analysis by considering mistyped words in automated essay scoring
| Autor: | Almodad Biduk Asmani, Martin Sendra, Rudy Sutrisno, Josep Harianata, Derwin Suhartono |
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| Rok vydání: | 2016 |
| Předmět: |
Grammar
business.industry Latent semantic analysis Computer science media_common.quotation_subject Closeness Document-term matrix Automated essay scoring computer.software_genre Human judgment Semantics Artificial intelligence business Value (mathematics) computer Natural language processing media_common |
| Zdroj: | 2016 International Conference on Informatics and Computing (ICIC). |
| DOI: | 10.1109/iac.2016.7905734 |
| Popis: | In this paper, we present an approach to consider the mistyped words which frequently occur in an essay. Not all the mistyped words are totally wrong. If they are caused by human errors, we should look further to handle it. Thus, enhanced version of Latent Semantic Analysis (LSA) is proposed. LSA is enhanced by calculating the value of mistyped words before constructing the term document matrix. Essays which are scored consists of 119 English essays of student's writing assignment while several essays as gold standard are made by experts. All of the essays have been scored manually by the human grader. Enhanced LSA gives the closeness level of 0.242 to human judgment, while LSA indicates the closeness level of 0.244 to human judgment. Unfortunately, Enhanced LSA cannot outperform GLSA which is one of current methods. The experiment result indicates that Enhanced LSA by considering mistyped words has a better closeness value to human judgment compared with LSA |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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