Using the Relative Entropy of Linguistic Complexity to Assess L2 Language Proficiency Development
This study applies relative entropy in naturalistic large-scale corpus to calculate the difference among L2 (second language) learners at different levels. We chose lemma, token, POS-trigram, conjunction to represent lexicon and grammar to detect the patterns of language proficiency development amon...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/8/1080 |
| _version_ | 1797523906331934720 |
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| author | Kun Sun Rong Wang |
| author_facet | Kun Sun Rong Wang |
| author_sort | Kun Sun |
| collection | DOAJ |
| description | This study applies relative entropy in naturalistic large-scale corpus to calculate the difference among L2 (second language) learners at different levels. We chose lemma, token, POS-trigram, conjunction to represent lexicon and grammar to detect the patterns of language proficiency development among different L2 groups using relative entropy. The results show that information distribution discrimination regarding lexical and grammatical differences continues to increase from L2 learners at a lower level to those at a higher level. This result is consistent with the assumption that in the course of second language acquisition, L2 learners develop towards a more complex and diverse use of language. Meanwhile, this study uses the statistics method of <i>time series</i> to process the data on L2 differences yielded by traditional frequency-based methods processing the same L2 corpus to compare with the results of relative entropy. However, the results from the traditional methods rarely show regularity. As compared to the algorithms in traditional approaches, relative entropy performs much better in detecting L2 proficiency development. In this sense, we have developed an effective and practical algorithm for stably detecting and predicting the developments in L2 learners’ language proficiency. |
| first_indexed | 2024-03-10T08:49:46Z |
| format | Article |
| id | doaj.art-71e233b9842a44199ca9c24504c0b1b2 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T08:49:46Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-71e233b9842a44199ca9c24504c0b1b22023-11-22T07:36:02ZengMDPI AGEntropy1099-43002021-08-01238108010.3390/e23081080Using the Relative Entropy of Linguistic Complexity to Assess L2 Language Proficiency DevelopmentKun Sun0Rong Wang1Department of Linguistics, University of Tübingen, 72074 Tübingen, GermanyInstitute of Natural Language Processing, University of Stuttgart, 70569 Stuttgart, GermanyThis study applies relative entropy in naturalistic large-scale corpus to calculate the difference among L2 (second language) learners at different levels. We chose lemma, token, POS-trigram, conjunction to represent lexicon and grammar to detect the patterns of language proficiency development among different L2 groups using relative entropy. The results show that information distribution discrimination regarding lexical and grammatical differences continues to increase from L2 learners at a lower level to those at a higher level. This result is consistent with the assumption that in the course of second language acquisition, L2 learners develop towards a more complex and diverse use of language. Meanwhile, this study uses the statistics method of <i>time series</i> to process the data on L2 differences yielded by traditional frequency-based methods processing the same L2 corpus to compare with the results of relative entropy. However, the results from the traditional methods rarely show regularity. As compared to the algorithms in traditional approaches, relative entropy performs much better in detecting L2 proficiency development. In this sense, we have developed an effective and practical algorithm for stably detecting and predicting the developments in L2 learners’ language proficiency.https://www.mdpi.com/1099-4300/23/8/1080L2 learnerslinguistic complexitylanguage proficiency developmentinformation theorytime series |
| spellingShingle | Kun Sun Rong Wang Using the Relative Entropy of Linguistic Complexity to Assess L2 Language Proficiency Development Entropy L2 learners linguistic complexity language proficiency development information theory time series |
| title | Using the Relative Entropy of Linguistic Complexity to Assess L2 Language Proficiency Development |
| title_full | Using the Relative Entropy of Linguistic Complexity to Assess L2 Language Proficiency Development |
| title_fullStr | Using the Relative Entropy of Linguistic Complexity to Assess L2 Language Proficiency Development |
| title_full_unstemmed | Using the Relative Entropy of Linguistic Complexity to Assess L2 Language Proficiency Development |
| title_short | Using the Relative Entropy of Linguistic Complexity to Assess L2 Language Proficiency Development |
| title_sort | using the relative entropy of linguistic complexity to assess l2 language proficiency development |
| topic | L2 learners linguistic complexity language proficiency development information theory time series |
| url | https://www.mdpi.com/1099-4300/23/8/1080 |
| work_keys_str_mv | AT kunsun usingtherelativeentropyoflinguisticcomplexitytoassessl2languageproficiencydevelopment AT rongwang usingtherelativeentropyoflinguisticcomplexitytoassessl2languageproficiencydevelopment |