Data Collection for Natural Language Processing Systems
- 226 Downloads
Any NLP system needs enough data for training and testing purposes. They can be split into two datasets: correct and incorrect (erroneous) data. Usually, it is not a problem to find and get a set of correct data because the correct texts are available from different sources, although they may also contain some mistakes. On the other hand, it is a hard task to get data containing errors like typos, mistakes and misspellings. This kind of data is usually obtained by a lengthy manual process and it requires annotation by human. One way to get the incorrect dataset faster is to generate it. However, this creates a problem how to generate incorrect texts so that they correspond to real human mistakes. In this paper, we focused on getting the incorrect dataset by help of humans. We created an automated web application (a game) that allows to collect incorrect texts and misspellings from players for texts written in the Slovak language. Based on the obtained data, we built a model of common errors that can be used to generate a large amount of authentic looking erroneous texts.
KeywordsData collection Typos Automatic text correction Spelling and typing model Typing game
This article was created in the framework of the National project IT Academy – Education for the 21st Century, which is supported by the European Social Fund and the European Regional Development Fund in the framework of the Operational Programme Human Resources.
- 1.Rodrigues, P., Rytting, C.A.: Typing race games as a method to create spelling error corpora. In: International Conference on Language Resources and Evaluation (LREC), Istanbul (2012)Google Scholar
- 2.Tachibana, R., Komachi, M.: Analysis of English spelling errors in a word-typing game. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Portorož, Slovenia (2016)Google Scholar
- 3.The International Arcade Museum: The Typing Of The Dead, WebMagic Ventures. https://www.arcade-museum.com/game_detail.php?game_id=10244. Accessed 27 Sept 2019
- 4.TypeRacer. https://data.typeracer.com/misc/about. Accessed 27 Sept 2019
- 5.Szablewski, D.: ZType - Typing Game - Type to Shoot. https://zty.pe/. Accessed 27 Sept 2019
- 6.Typing.com: Typing Games. https://www.typing.com/student/games. Accessed 27 Sept 2019
- 7.Grundkiewicz, R., Junczys-Dowmunt, M.: The WikEd error corpus: a corpus of corrective wikipedia edits and its application to grammatical error correction. In: Przepiórkowski, A., Ogrodniczuk, M. (eds.) NLP 2014. LNCS (LNAI), vol. 8686, pp. 478–490. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10888-9_47CrossRefGoogle Scholar
- 8.Ľ. Štúr Institute of Linguistics of the Slovak Academy of Sciences: Error Corpus of Slovak CHIBY, 05 Aug 2019. https://www.juls.savba.sk/errcorp_en.html
- 9.The Wikimedia Foundation: skwiki dump. https://dumps.wikimedia.org/skwiki/latest/
- 10.Attardi, G.: WikiExtractor - Python script that extracts and cleans text from a Wikipedia database dump. https://github.com/attardi/wikiextractor