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https://ptsldigital.ukm.my/jspui/handle/123456789/476342Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Nazlia Omar, Prof. Dr. | |
| dc.contributor.author | Rawad Mezher Abed (P72220) | |
| dc.date.accessioned | 2023-10-06T09:16:44Z | - |
| dc.date.available | 2023-10-06T09:16:44Z | - |
| dc.date.issued | 2016-02-04 | |
| dc.identifier.other | ukmvital:82205 | |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/476342 | - |
| dc.description | Assessment plays a significant role in the educational process. Automated Essay Scoring (AES) is defined as the computer technology that evaluates and scores the subjective answers. Unlike Multiple Choice Question (MCQ), essays contain subjective answers rather than the factual answers in MCQ (e.g. true or false). Therefore, the process of automated essays assessments is a challenging task due to the need of comprehensive evaluation in order to validate the answers accurately. The challenge increases when dealing with Arabic language where morphology, semantic and syntactic are complex. There are few research efforts have been proposed for Automatic Essays Scoring (AES) in Arabic. However, such efforts have concentrated on the semantic perspective by proposing Latent Semantic Analysis (LSA). LSA is based on word-document co-occurrence, also called as 'bag-of-words' approach. It is therefore blind to the syntactic information. This puts limitations on LSA’s ability to capture the meaning of a sentence which depends upon both syntax and semantic. Thus, using syntactical features may improve the process of evaluation. Hence, this study proposed a hybrid method of syntactic features and LSA for automatic essay scoring. Several preprocessing tasks have been performed in order to normalize the words with an appropriate format for processing. The similarity matrix of LSA will be constructed using Term Frequency-Inverse Document Frequency (TF-IDF). Consequentially, the cosine similarity is carried out to identify the similarity among words. Finally, Part-Of-Speech (POS) tagging is applied in order to identify the syntactic feature of words within the similarity matrix. A dataset contains 61 questions related to Environmental Science with 10 answers for each question where the total number of answers is 610, was used in the experiments. The evaluation has been performed using Root Mean Squared Error (RMSE) in which the Euclidean distance is being computed between the manual score (i.e. given by the teacher) and the automatic score (i.e. given by the proposed method). Results shown that the syntactical LSA has outperformed the standard LSA by obtaining 0.268 of RMSE. This showns that the syntactic feature improves the accuracy of AES for Arabic language.,Certification of Master's/Doctoral Thesis" is not available | |
| dc.language.iso | eng | |
| dc.publisher | UKM, Bangi | |
| dc.relation | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
| dc.rights | UKM | |
| dc.subject | Automated Essay Scoring | |
| dc.subject | Arabic essay | |
| dc.subject | Hybrid method | |
| dc.subject | Syntactic features | |
| dc.subject | Dissertations, Academic -- Malaysia | |
| dc.title | A hyrbid method of syntactic feature and latent semantic analysis for automatic Arabic essay scoring | |
| dc.type | theses | |
| dc.format.pages | 151 | |
| dc.identifier.barcode | 002318(2016) | |
| Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_82205+SOURCE1+SOURCE1.0.PDF Restricted Access | 282.28 kB | Adobe PDF |  View/Open |
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