{"title":"Announcing changes to our editorial team and editorial board","authors":"","doi":"10.1075/ijlcr.00020.edi","DOIUrl":"https://doi.org/10.1075/ijlcr.00020.edi","url":null,"abstract":"","PeriodicalId":29715,"journal":{"name":"International Journal of Learner Corpus Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44561060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article reports on an open-source R package for the extraction of syntactic units from dependency-parsed French texts. To evaluate the reliability of the package, syntactic units were extracted from a corpus of L2 French and were compared to units extracted manually from the same corpus. The f-score of the extracted units ranged from 0.53–0.97. Although units were not always identical between the two methods, manual and automatically-derived syntactic complexity measures were strongly and significantly correlated (ρ = 0.62–0.97, p < 0.001), suggesting that this package may be a suitable replacement for manual annotation in some cases where manual annotation is not possible but that care should be used in interpreting the measures based on these units.
{"title":"fsca","authors":"Nathan Vandeweerd","doi":"10.1075/ijlcr.20018.van","DOIUrl":"https://doi.org/10.1075/ijlcr.20018.van","url":null,"abstract":"\u0000 This article reports on an open-source R package for the extraction of syntactic units from dependency-parsed\u0000 French texts. To evaluate the reliability of the package, syntactic units were extracted from a corpus of L2 French and were\u0000 compared to units extracted manually from the same corpus. The f-score of the extracted units ranged from 0.53–0.97. Although\u0000 units were not always identical between the two methods, manual and automatically-derived syntactic complexity measures were\u0000 strongly and significantly correlated (ρ = 0.62–0.97, p < 0.001), suggesting that this\u0000 package may be a suitable replacement for manual annotation in some cases where manual annotation is not possible but that care\u0000 should be used in interpreting the measures based on these units.","PeriodicalId":29715,"journal":{"name":"International Journal of Learner Corpus Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43471896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nadine Herry-Bénit, Stéphanie Lopez, Takeki Kamiyama, J. Tennant
This article presents the IPCE-IPAC corpus, an ongoing project, which has been collected in France, Italy, Spain and China since 2014. The data is collected to investigate the acquisition of segmental and suprasegmental phenomena by L2 learners of English, with a focus on phonemes. The article discusses the methods for the compilation of this original spoken learner corpus, designed to study L2 “interphonology” (Detey, Racine, Kawaguchi, & Zay, 2016), or interlanguage phonology.
{"title":"The interphonology of contemporary English corpus (IPCE-IPAC)","authors":"Nadine Herry-Bénit, Stéphanie Lopez, Takeki Kamiyama, J. Tennant","doi":"10.1075/ijlcr.20010.her","DOIUrl":"https://doi.org/10.1075/ijlcr.20010.her","url":null,"abstract":"\u0000 This article presents the IPCE-IPAC corpus, an ongoing project, which has been collected in France, Italy, Spain\u0000 and China since 2014. The data is collected to investigate the acquisition of segmental and suprasegmental phenomena by L2\u0000 learners of English, with a focus on phonemes. The article discusses the methods for the compilation of this original spoken\u0000 learner corpus, designed to study L2 “interphonology” (Detey, Racine, Kawaguchi, & Zay,\u0000 2016), or interlanguage phonology.","PeriodicalId":29715,"journal":{"name":"International Journal of Learner Corpus Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41700012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study partially replicates Paquot’s (2018, 2019) study of phraseological complexity in L2 English by investigating how phraseological complexity compares across proficiency levels as well as how phraseological complexity measures relate to lexical, syntactic and morphological complexity measures in a corpus of L2 French argumentative essays. Phraseological complexity is operationalized as the diversity (root type-token ratio; RTTR) and sophistication (pointwise mutual information; PMI) of three types of grammatical dependencies: adjectival modifiers, adverbial modifiers and direct objects. Results reveal a significant increase in the mean PMI of direct objects and the RTTR of adjectival modifiers across proficiency levels. In addition to phraseological sophistication, important predictors of proficiency include measures of lexical diversity, lexical sophistication, syntactic (phrasal) complexity and morphological complexity. The results provide cross-linguistic validation for the results of Paquot (2018, 2019) and further highlight the importance of including phraseological measures in the current repertoire of L2 complexity measures.
{"title":"Applying phraseological complexity measures to L2 French","authors":"Nathan Vandeweerd, Alex Housen, M. Paquot","doi":"10.1075/ijlcr.20015.van","DOIUrl":"https://doi.org/10.1075/ijlcr.20015.van","url":null,"abstract":"\u0000 This study partially replicates Paquot’s (2018, 2019) study of phraseological complexity in L2 English by investigating how phraseological complexity\u0000 compares across proficiency levels as well as how phraseological complexity measures relate to lexical, syntactic and\u0000 morphological complexity measures in a corpus of L2 French argumentative essays. Phraseological complexity is operationalized as\u0000 the diversity (root type-token ratio; RTTR) and sophistication (pointwise mutual information; PMI) of three\u0000 types of grammatical dependencies: adjectival modifiers, adverbial modifiers and direct objects. Results reveal a significant\u0000 increase in the mean PMI of direct objects and the RTTR of adjectival modifiers across proficiency levels. In\u0000 addition to phraseological sophistication, important predictors of proficiency include measures of lexical diversity, lexical\u0000 sophistication, syntactic (phrasal) complexity and morphological complexity. The results provide cross-linguistic validation for\u0000 the results of Paquot (2018, 2019) and\u0000 further highlight the importance of including phraseological measures in the current repertoire of L2 complexity measures.","PeriodicalId":29715,"journal":{"name":"International Journal of Learner Corpus Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46967053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paloma Fernández-Mira, Emily Morgan, Sam Davidson, Aaron Yamada, Agustina Carando, Kenji Sagae, C. Sánchez-Gutiérrez
This study examines the impact of two topic-related variables (i.e., valence polarity and everyday-life closeness) on the lexical diversity scores (i.e., MTLD) of learners of L2 Spanish at different proficiency levels. The analysis included 3,045 texts written in response to two pairs of prompts by 1,165 students enrolled in an L2 Spanish program. The first pair of prompts asked learners to narrate an event: prompt 1 focused on a perfect vacation (positive event), while prompt 2 asked participants to tell a terrible story (negative event). The second pair asked to describe a person: prompt 1 required that the subject be famous, thus not close to the writer, whereas prompt 2 required that the subject be special and close to the writer. Results indicate that lexical diversity scores were higher for the texts written about the positive event and the famous subject across all proficiency levels.
{"title":"Lexical diversity in an L2 Spanish learner corpus","authors":"Paloma Fernández-Mira, Emily Morgan, Sam Davidson, Aaron Yamada, Agustina Carando, Kenji Sagae, C. Sánchez-Gutiérrez","doi":"10.1075/ijlcr.20017.fer","DOIUrl":"https://doi.org/10.1075/ijlcr.20017.fer","url":null,"abstract":"\u0000 This study examines the impact of two topic-related variables (i.e., valence polarity and everyday-life closeness)\u0000 on the lexical diversity scores (i.e., MTLD) of learners of L2 Spanish at different proficiency levels. The analysis included\u0000 3,045 texts written in response to two pairs of prompts by 1,165 students enrolled in an L2 Spanish program. The first pair of\u0000 prompts asked learners to narrate an event: prompt 1 focused on a perfect vacation (positive event), while prompt 2 asked\u0000 participants to tell a terrible story (negative event). The second pair asked to describe a person: prompt 1 required that the\u0000 subject be famous, thus not close to the writer, whereas prompt 2 required that the subject be special and close to the writer.\u0000 Results indicate that lexical diversity scores were higher for the texts written about the positive event and the famous subject\u0000 across all proficiency levels.","PeriodicalId":29715,"journal":{"name":"International Journal of Learner Corpus Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45369561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Review of Schilk (2020): Language Processing in Advanced Learners of English: A Multi-method Approach to Collocation Based on Corpus Linguistics and Experimental Data","authors":"J. Garner","doi":"10.1075/ijlcr.00021.gar","DOIUrl":"https://doi.org/10.1075/ijlcr.00021.gar","url":null,"abstract":"","PeriodicalId":29715,"journal":{"name":"International Journal of Learner Corpus Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43328644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Review of Götz & Mukherjee (2019): Learner Corpora and Language Teaching","authors":"T. Rankin","doi":"10.1075/ijlcr.00022.ran","DOIUrl":"https://doi.org/10.1075/ijlcr.00022.ran","url":null,"abstract":"","PeriodicalId":29715,"journal":{"name":"International Journal of Learner Corpus Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41602419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Natural language processing for learner corpus research","authors":"K. Kyle","doi":"10.1075/ijlcr.00019.int","DOIUrl":"https://doi.org/10.1075/ijlcr.00019.int","url":null,"abstract":"","PeriodicalId":29715,"journal":{"name":"International Journal of Learner Corpus Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46021370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicolas Ballier, S. Canu, C. Petitjean, G. Gasso, C. Balhana, T. Alexopoulou, Thomas Gaillat
This paper discusses machine learning techniques for the prediction of Common European Framework of Reference (CEFR) levels in a learner corpus. We summarise the CAp 2018 Machine Learning (ML) competition, a classification task of the six CEFR levels, which map linguistic competence in a foreign language onto six reference levels. The goal of this competition was to produce a machine learning system to predict learners’ competence levels from written productions comprising between 20 and 300 words and a set of characteristics computed for each text extracted from the French component of the EFCAMDAT data (Geertzen et al., 2013). Together with the description of the competition, we provide an analysis of the results and methods proposed by the participants and discuss the benefits of this kind of competition for the learner corpus research (LCR) community. The main findings address the methods used and lexical bias introduced by the task.
{"title":"Machine learning for learner English","authors":"Nicolas Ballier, S. Canu, C. Petitjean, G. Gasso, C. Balhana, T. Alexopoulou, Thomas Gaillat","doi":"10.1075/ijlcr.18012.bal","DOIUrl":"https://doi.org/10.1075/ijlcr.18012.bal","url":null,"abstract":"\u0000 This paper discusses machine learning techniques for the prediction of Common European Framework of Reference (CEFR)\u0000 levels in a learner corpus. We summarise the CAp 2018 Machine Learning (ML) competition, a\u0000 classification task of the six CEFR levels, which map linguistic competence in a foreign language onto six reference levels. The goal of\u0000 this competition was to produce a machine learning system to predict learners’ competence levels from written productions comprising between\u0000 20 and 300 words and a set of characteristics computed for each text extracted from the French component of the EFCAMDAT data (Geertzen et al., 2013). Together with the description of the competition, we provide an analysis of\u0000 the results and methods proposed by the participants and discuss the benefits of this kind of competition for the learner corpus research\u0000 (LCR) community. The main findings address the methods used and lexical bias introduced by the task.","PeriodicalId":29715,"journal":{"name":"International Journal of Learner Corpus Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2020-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42428715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
From the perspective of the compilers, a corpus is a journey. This particular journey – the process of the design and compilation of the Trinity Lancaster Corpus (TLC), the largest spoken learner corpus of (interactive) English to date – took over five years. It involved more than 3,500 hours of transcription time1 with many more hours spent on quality checking and post-processing of the data. This simple statistic shows why learner corpora of spoken language are still relatively rare, despite the fact that they provide a unique insight into spontaneous language production (McEnery, Brezina, Gablasova & Banerjee 2019). While the advances in computational technology allow better data processing and more efficient analysis, the starting point of a spoken (learner) corpus is still the recording of speech and its manual transcription. This method is considerably more reliable in capturing the details of spoken language than any existing voice recognition system. This is true for spoken L1 (McEnery 2018) as well as spoken L2 data (Gilquin 2015). The difference between the performance of an experienced transcriber and a state-ofthe-art automated system is immediately obvious from the comparison shown in Table 1. For meaningful linguistic analysis, only the sample transcript shown on the left (from the TLC) is suitable as it represents an accurate account of the spoken production. Building a spoken learner corpus is thus a resource-intensive project. The compilation of the TLC was made possible by research collaboration between Lancaster University and Trinity College London, a major international testing board. The project was supported by the Economic and Social Research Council (ESRC) and Trinity College London.2
{"title":"Corpus-based Approaches to Spoken L2 Production","authors":"V. Brezina, Dana Gablasova, Tony McEnery","doi":"10.1075/ijlcr.00008.int","DOIUrl":"https://doi.org/10.1075/ijlcr.00008.int","url":null,"abstract":"From the perspective of the compilers, a corpus is a journey. This particular journey – the process of the design and compilation of the Trinity Lancaster Corpus (TLC), the largest spoken learner corpus of (interactive) English to date – took over five years. It involved more than 3,500 hours of transcription time1 with many more hours spent on quality checking and post-processing of the data. This simple statistic shows why learner corpora of spoken language are still relatively rare, despite the fact that they provide a unique insight into spontaneous language production (McEnery, Brezina, Gablasova & Banerjee 2019). While the advances in computational technology allow better data processing and more efficient analysis, the starting point of a spoken (learner) corpus is still the recording of speech and its manual transcription. This method is considerably more reliable in capturing the details of spoken language than any existing voice recognition system. This is true for spoken L1 (McEnery 2018) as well as spoken L2 data (Gilquin 2015). The difference between the performance of an experienced transcriber and a state-ofthe-art automated system is immediately obvious from the comparison shown in Table 1. For meaningful linguistic analysis, only the sample transcript shown on the left (from the TLC) is suitable as it represents an accurate account of the spoken production. Building a spoken learner corpus is thus a resource-intensive project. The compilation of the TLC was made possible by research collaboration between Lancaster University and Trinity College London, a major international testing board. The project was supported by the Economic and Social Research Council (ESRC) and Trinity College London.2","PeriodicalId":29715,"journal":{"name":"International Journal of Learner Corpus Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2019-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48005646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：