@inproceedings{berkling-etal-2014-database,
title = "A Database of Freely Written Texts of {G}erman School Students for the Purpose of Automatic Spelling Error Classification",
author = {Berkling, Kay and
Fay, Johanna and
Ghayoomi, Masood and
Hein, Katrin and
Lavalley, R{\'e}mi and
Linhuber, Ludwig and
St{\"u}ker, Sebastian},
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/255_Paper.pdf",
pages = "1212--1217",
abstract = "The spelling competence of school students is best measured on freely written texts, instead of pre-determined, dictated texts. Since the analysis of the error categories in these kinds of texts is very labor intensive and costly, we are working on an automatic systems to perform this task. The modules of the systems are derived from techniques from the area of natural language processing, and are learning systems that need large amounts of training data. To obtain the data necessary for training and evaluating the resulting system, we conducted data collection of freely written, German texts by school children. 1,730 students from grade 1 through 8 participated in this data collection. The data was transcribed electronically and annotated with their corrected version. This resulted in a total of 14,563 sentences that can now be used for research regarding spelling diagnostics. Additional meta-data was collected regarding writers{'} language biography, teaching methodology, age, gender, and school year. In order to do a detailed manual annotation of the categories of the spelling errors committed by the students we developed a tool specifically tailored to the task.",
}
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<abstract>The spelling competence of school students is best measured on freely written texts, instead of pre-determined, dictated texts. Since the analysis of the error categories in these kinds of texts is very labor intensive and costly, we are working on an automatic systems to perform this task. The modules of the systems are derived from techniques from the area of natural language processing, and are learning systems that need large amounts of training data. To obtain the data necessary for training and evaluating the resulting system, we conducted data collection of freely written, German texts by school children. 1,730 students from grade 1 through 8 participated in this data collection. The data was transcribed electronically and annotated with their corrected version. This resulted in a total of 14,563 sentences that can now be used for research regarding spelling diagnostics. Additional meta-data was collected regarding writers’ language biography, teaching methodology, age, gender, and school year. In order to do a detailed manual annotation of the categories of the spelling errors committed by the students we developed a tool specifically tailored to the task.</abstract>
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%0 Conference Proceedings
%T A Database of Freely Written Texts of German School Students for the Purpose of Automatic Spelling Error Classification
%A Berkling, Kay
%A Fay, Johanna
%A Ghayoomi, Masood
%A Hein, Katrin
%A Lavalley, Rémi
%A Linhuber, Ludwig
%A Stüker, Sebastian
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F berkling-etal-2014-database
%X The spelling competence of school students is best measured on freely written texts, instead of pre-determined, dictated texts. Since the analysis of the error categories in these kinds of texts is very labor intensive and costly, we are working on an automatic systems to perform this task. The modules of the systems are derived from techniques from the area of natural language processing, and are learning systems that need large amounts of training data. To obtain the data necessary for training and evaluating the resulting system, we conducted data collection of freely written, German texts by school children. 1,730 students from grade 1 through 8 participated in this data collection. The data was transcribed electronically and annotated with their corrected version. This resulted in a total of 14,563 sentences that can now be used for research regarding spelling diagnostics. Additional meta-data was collected regarding writers’ language biography, teaching methodology, age, gender, and school year. In order to do a detailed manual annotation of the categories of the spelling errors committed by the students we developed a tool specifically tailored to the task.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/255_Paper.pdf
%P 1212-1217
Markdown (Informal)
[A Database of Freely Written Texts of German School Students for the Purpose of Automatic Spelling Error Classification](http://www.lrec-conf.org/proceedings/lrec2014/pdf/255_Paper.pdf) (Berkling et al., LREC 2014)
ACL