Using corpora from Natural Language Processing for investigating crosslinguistic influence

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AEXUUBARJ" target="_blank" >RIV/00216208:11320/25:EXUUBARJ - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194090314&doi=10.1016%2fj.amper.2024.100174&partnerID=40&md5=e72f1d74ff9909c7fd8b9606094d0fa8" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194090314&doi=10.1016%2fj.amper.2024.100174&partnerID=40&md5=e72f1d74ff9909c7fd8b9606094d0fa8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.amper.2024.100174" target="_blank" >10.1016/j.amper.2024.100174</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using corpora from Natural Language Processing for investigating crosslinguistic influence

Popis výsledku v původním jazyce

Language transfer or crosslinguistic influence (CLI), referring to the influence of an L1 on the learning of an L2, is a significant aspect of Second Language Acquisition (SLA). Much work in this area is data-driven, and consequently, large L2 corpora have been constructed for use in CLI analyses. The field of Natural Language Processing, and in particular the specific task of Grammatical Error Correction (GEC), also has corpora that can be of use in these kinds of analyses. In this paper, we take the FCE corpus, a popular dataset of English as a Second Language (ESL) learner texts used for Grammatical Error Correction model training, and use it to analyse the relationship between the distributions of errors and the first languages of the ESL learners. We carry out a detailed analysis of three error types, and demonstrate that the errors made by ESL learners have a statistically significant relationship with linguistic characteristics of their first languages, suggesting the existence of both positive and negative transfer. The analysis aligns with results from the SLA literature, and validates the use of GEC corpora for use in CLI analysis. © 2024 The Authors

Název v anglickém jazyce

Using corpora from Natural Language Processing for investigating crosslinguistic influence

Popis výsledku anglicky

Language transfer or crosslinguistic influence (CLI), referring to the influence of an L1 on the learning of an L2, is a significant aspect of Second Language Acquisition (SLA). Much work in this area is data-driven, and consequently, large L2 corpora have been constructed for use in CLI analyses. The field of Natural Language Processing, and in particular the specific task of Grammatical Error Correction (GEC), also has corpora that can be of use in these kinds of analyses. In this paper, we take the FCE corpus, a popular dataset of English as a Second Language (ESL) learner texts used for Grammatical Error Correction model training, and use it to analyse the relationship between the distributions of errors and the first languages of the ESL learners. We carry out a detailed analysis of three error types, and demonstrate that the errors made by ESL learners have a statistically significant relationship with linguistic characteristics of their first languages, suggesting the existence of both positive and negative transfer. The analysis aligns with results from the SLA literature, and validates the use of GEC corpora for use in CLI analysis. © 2024 The Authors

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Ampersand

ISSN

2215-0390

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

1-10

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85194090314