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Task Effects on Linguistic Complexity and Accuracy: A Large‐Scale Learner Corpus Analysis Employing Natural Language Processing Techniques
Author(s) -
Alexopoulou Theodora,
Michel Marije,
Murakami Akira,
Meurers Detmar
Publication year - 2017
Publication title -
language learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.882
H-Index - 103
eISSN - 1467-9922
pISSN - 0023-8333
DOI - 10.1111/lang.12232
Subject(s) - task (project management) , computer science , natural language processing , task analysis , artificial intelligence , scale (ratio) , computational linguistics , language identification , linguistics , natural language , corpus linguistics , language acquisition , second language acquisition , philosophy , physics , management , quantum mechanics , economics
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: How does 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.