Refining and modifying the EFCAMDAT: Lessons from creating a new corpus from an existing large-scale English learner language database
Abstract
This report outlines the development of a new corpus, which was created by refining and modifying the largest open-access L2 English learner database – the EFCAMDAT. The extensive data-curation process, which can inform the development and use of other corpora, included procedures such as converting the database from XML to a tabular format, and removing problematic markup tags and non-English texts. The final dataset contains two corresponding samples, written by similar learners in response to different prompts, which represents a unique research opportunity when it comes to analyzing task effects and conducting replication studies. Overall, the resulting corpus contains ~406,000 texts in the first sample and ~317,000 texts in the second sample, written by learners representing diverse L1s and a large range of L2 proficiency levels.
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Shatz (2020) estimated the correct topics of writings in EFCAMDAT.
“Shatz (2020) estimated the correct topics of writings in EFCAMDAT”
From Effects of Availability, Contingency, and Formulaicity on the Accuracy of English Grammatical Morphemes in Second Language Writing (2022) · cites this paper as Shatz (2020)
The study used topic IDs from Shatz (2020) rather than original EFCAMDAT IDs to account for topic effects.
“we used the topic IDs in Shatz (2020) rather than the original IDs in accounting for topic effects”
From Effects of Availability, Contingency, and Formulaicity on the Accuracy of English Grammatical Morphemes in Second Language Writing (2022) · cites this paper as Shatz (2020)
Cites
Dependency parsing of learner English
Huang, Y., Murakami, A., Alexopoulou, T., & Korhonen, A. (2018) · International Journal of Corpus Linguistics · doi:10.1075/ijcl.16080.hua · Cited by 68 · see citing works
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Murakami, A., & Ellis, N. C. (2022) · Language Learning · doi:10.1111/lang.12500 · Cited by 26 · see citing works
- Authors
- Shatz, I.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Shatz, Itamar
- ORCID
- Shatz, Itamar
- Venue
- International Journal of Learner Corpus Research
- Publisher
- John Benjamins Publishing Company
- Published
- 2020-12-10
- Volume
- 6
- Issue
- 2
- Pages
- 220-236
- ISSN
- 2215-1478 (ISSN-L)
- 2215-1486
- OpenAlex
- W3089600100
- Cited by
- 20
- References
- 1
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 5 · 2023: 1 · 2022: 4
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Learner Corpus Research (publishedVersion)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Second Language Acquisition and Learning
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Discourse Analysis in Language Studies
- Keywords
- learner corpus, corpus development, data curation, task effects, replication study, second language proficiency, learner writing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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