Task effects on linguistic complexity and accuracy: a large-scale learner corpus analysis employing Natural Language Processing techniques

Theodora Alexopoulou; Marije  Michel; Akira Murakami; Meurers Detmar

doi:10.1111/lang.12232

Task effects on linguistic complexity and accuracy: a large-scale learner corpus analysis employing Natural Language Processing techniques

Theodora Alexopoulou^*
, Marije Michel
, Akira Murakami
, Meurers Detmar

^*Corresponding author for this work

Lancaster University

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Large-scale learner corpora collected from online language learning platforms, such as the EF-Cambridge Open Language Database (EFCAMDAT), provide opportunities to analyze learner data at an unprecedented scale. However, interpreting the learner language in such corpora requires a precise understanding of tasks: Howdoes the prompt and input of a task and its functional requirements influence task-based linguistic performance?
This question is vital for making large-scale task-based corpora fruitful for second language acquisition research. We explore the issue through an analysis of selected tasks in EFCAMDAT and the complexity and accuracy of the language they elicit.

Original language	English
Pages (from-to)	180-208
Number of pages	29
Journal	Language Learning
Volume	67
Issue number	Suppl. 1
DOIs	https://doi.org/10.1111/lang.12232
Publication status	Published - 2017
Externally published	Yes

Keywords

s learner corpus
task complexity
complexity
accuracy
fluency (CAF)
NLP
TBLT

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10.1111/lang.12232

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title = "Task effects on linguistic complexity and accuracy: a large-scale learner corpus analysis employing Natural Language Processing techniques",

abstract = "Large-scale learner corpora collected from online language learning platforms, such as the EF-Cambridge Open Language Database (EFCAMDAT), provide opportunities to analyze learner data at an unprecedented scale. However, interpreting the learner language in such corpora requires a precise understanding of tasks: Howdoes the prompt and input of a task and its functional requirements influence task-based linguistic performance? This question is vital for making large-scale task-based corpora fruitful for second language acquisition research. We explore the issue through an analysis of selected tasks in EFCAMDAT and the complexity and accuracy of the language they elicit.",

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author = "Theodora Alexopoulou and Marije Michel and Akira Murakami and Meurers Detmar",

year = "2017",

doi = "10.1111/lang.12232",

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AU - Alexopoulou, Theodora

AU - Michel, Marije

AU - Murakami, Akira

AU - Detmar, Meurers

PY - 2017

Y1 - 2017

N2 - Large-scale learner corpora collected from online language learning platforms, such as the EF-Cambridge Open Language Database (EFCAMDAT), provide opportunities to analyze learner data at an unprecedented scale. However, interpreting the learner language in such corpora requires a precise understanding of tasks: Howdoes the prompt and input of a task and its functional requirements influence task-based linguistic performance? This question is vital for making large-scale task-based corpora fruitful for second language acquisition research. We explore the issue through an analysis of selected tasks in EFCAMDAT and the complexity and accuracy of the language they elicit.

AB - Large-scale learner corpora collected from online language learning platforms, such as the EF-Cambridge Open Language Database (EFCAMDAT), provide opportunities to analyze learner data at an unprecedented scale. However, interpreting the learner language in such corpora requires a precise understanding of tasks: Howdoes the prompt and input of a task and its functional requirements influence task-based linguistic performance? This question is vital for making large-scale task-based corpora fruitful for second language acquisition research. We explore the issue through an analysis of selected tasks in EFCAMDAT and the complexity and accuracy of the language they elicit.

KW - s learner corpus

KW - task complexity

KW - complexity

KW - accuracy

KW - fluency (CAF)

KW - NLP

KW - TBLT

U2 - 10.1111/lang.12232

DO - 10.1111/lang.12232

M3 - Article

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EP - 208

JO - Language Learning

JF - Language Learning

IS - Suppl. 1

ER -

Task effects on linguistic complexity and accuracy: a large-scale learner corpus analysis employing Natural Language Processing techniques

Abstract

Keywords

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