Application of Computational Linguistics to Predicting Language Proficiency Level of Persian Learners’ Textbooks
One subfield of assessment of language proficiency is predicting language proficiency level.This research aims at proposing a computational linguistic model to predict language proficiency level and to explore the general properties of the levels. To this end, a corpus is developed from Persian lear...
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| Format: | Article |
| Language: | English |
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Alzahra University
2022-05-01
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| Series: | Journal of Language Horizons |
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| Online Access: | https://lghor.alzahra.ac.ir/article_5408_fa9d32e31cb704550c3da396d6a03405.pdf |
| _version_ | 1828796782429077504 |
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| author | Masood Ghayoomi |
| author_facet | Masood Ghayoomi |
| author_sort | Masood Ghayoomi |
| collection | DOAJ |
| description | One subfield of assessment of language proficiency is predicting language proficiency level.This research aims at proposing a computational linguistic model to predict language proficiency level and to explore the general properties of the levels. To this end, a corpus is developed from Persian learners' textbooks and statistical and linguistic features are extracted from this text corpus to train three classifiers as learners. The performance of the models vary based on the learning algorithm and the feature set(s) used for training the models. For evaluating the models, four standard metrics, namely accuracy, precision, recall, and F-measure were used. Based on the results, the model created by the Random Forest classifier performed the best when statistical features extracted from raw text is used. The Support Vector Machine classifier performed the best by using linguistic features extracted from the automatically annotated corpus. The results determine that enriching the model and providing various kinds of information do not guarantee that a classifier (learner) performs the best.To discover the latent teaching methodology of the textbooks, the general performance of the classifiers with respect to the language level and the linguistic knowledge used for creating the model are studied. Based on the obtained results, the amount of extracted features plays an important role in training a classifier. Furthermore, the average best performance of the classifiers is extending the linguistic knowledge from syntactic patterns at proficiency level A (beginner) to all linguistic information at levels B (intermediate) and C (advanced). |
| first_indexed | 2024-12-12T04:28:56Z |
| format | Article |
| id | doaj.art-3b5a424d0cfd467eae26ef34a8aa52ca |
| institution | Directory Open Access Journal |
| issn | 2588-350X 2588-5634 |
| language | English |
| last_indexed | 2024-12-12T04:28:56Z |
| publishDate | 2022-05-01 |
| publisher | Alzahra University |
| record_format | Article |
| series | Journal of Language Horizons |
| spelling | doaj.art-3b5a424d0cfd467eae26ef34a8aa52ca2022-12-22T00:38:08ZengAlzahra UniversityJournal of Language Horizons2588-350X2588-56342022-05-0161295210.22051/lghor.2021.32656.13545408Application of Computational Linguistics to Predicting Language Proficiency Level of Persian Learners’ TextbooksMasood Ghayoomi0Assistant Professor, Faculty of Linguistics, Institute for the Humanities and Cultural Studies, Tehran, Iran;One subfield of assessment of language proficiency is predicting language proficiency level.This research aims at proposing a computational linguistic model to predict language proficiency level and to explore the general properties of the levels. To this end, a corpus is developed from Persian learners' textbooks and statistical and linguistic features are extracted from this text corpus to train three classifiers as learners. The performance of the models vary based on the learning algorithm and the feature set(s) used for training the models. For evaluating the models, four standard metrics, namely accuracy, precision, recall, and F-measure were used. Based on the results, the model created by the Random Forest classifier performed the best when statistical features extracted from raw text is used. The Support Vector Machine classifier performed the best by using linguistic features extracted from the automatically annotated corpus. The results determine that enriching the model and providing various kinds of information do not guarantee that a classifier (learner) performs the best.To discover the latent teaching methodology of the textbooks, the general performance of the classifiers with respect to the language level and the linguistic knowledge used for creating the model are studied. Based on the obtained results, the amount of extracted features plays an important role in training a classifier. Furthermore, the average best performance of the classifiers is extending the linguistic knowledge from syntactic patterns at proficiency level A (beginner) to all linguistic information at levels B (intermediate) and C (advanced).https://lghor.alzahra.ac.ir/article_5408_fa9d32e31cb704550c3da396d6a03405.pdfmachine learningclassificationfeaturecomputational cognitive modelpersian learner |
| spellingShingle | Masood Ghayoomi Application of Computational Linguistics to Predicting Language Proficiency Level of Persian Learners’ Textbooks Journal of Language Horizons machine learning classification feature computational cognitive model persian learner |
| title | Application of Computational Linguistics to Predicting Language Proficiency Level of Persian Learners’ Textbooks |
| title_full | Application of Computational Linguistics to Predicting Language Proficiency Level of Persian Learners’ Textbooks |
| title_fullStr | Application of Computational Linguistics to Predicting Language Proficiency Level of Persian Learners’ Textbooks |
| title_full_unstemmed | Application of Computational Linguistics to Predicting Language Proficiency Level of Persian Learners’ Textbooks |
| title_short | Application of Computational Linguistics to Predicting Language Proficiency Level of Persian Learners’ Textbooks |
| title_sort | application of computational linguistics to predicting language proficiency level of persian learners textbooks |
| topic | machine learning classification feature computational cognitive model persian learner |
| url | https://lghor.alzahra.ac.ir/article_5408_fa9d32e31cb704550c3da396d6a03405.pdf |
| work_keys_str_mv | AT masoodghayoomi applicationofcomputationallinguisticstopredictinglanguageproficiencylevelofpersianlearnerstextbooks |