Refining and modifying the EFCAMDAT

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.

Utility of Kolmogorov complexity measures: Analysis of L2 groups and L1 backgrounds

“…Two L2 written corpora were used in the present study: MERLIN [77] and the EF Cambridge Open Language Database (EFCAMDAT) [78][79][80]. MERLIN includes texts written by L2 Czech, German and Italian speakers, while EFCAMDAT covers texts written by L2 English speakers.…”

“…As mentioned above, EFCAMDAT essays were all persuasive essays. EFCAMDAT is semi-longitudinal and contains several texts written by the same L2 learner [78]. This study selected only one text per L2 learner to reduce idiosyncratic complexity features.…”

Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

“…2 Datasets and setup 2.1 EFCAMDAT Firstly, we use the EFCAMDAT corpus (Geertzen et al, 2014) that comprises L2 learners' scripts annotated with their respective score on a scale from 0 to 100, their proficiency level from 1 to 16 (mapped to CEFR levels from A1 to C2) and partially error-tagged by human experts. As our work investigates the efficacy of errors as features, we only use the error-tagged section of the EFCAM-DAT Cleaned Subcorpus (Shatz, 2020), consisting of 498,208 scripts ranging from proficiency level 1 to 15 (i.e. from A1 to C1), which we divided into training and test set.…”