Abstract
The task of grammatical error correction is a widely studied field of natural language processing where the traditional rule-based approaches compete with the machine learning methods. The rule-based approach benefits mainly from a wide knowledge base available for a given language. On the contrary, the transfer learning methods and especially the use of pre-trained Transformers have the ability to be trained from a huge number of texts in a given language. In this paper, we focus on the task of automatic correction of missing commas in Czech written texts and we compare the rule-based approach with the Transformer-based model trained for this task.
This work was supported by the project of specific research Lexikon a gramatika češtiny II - 2022 (Lexicon and Grammar of Czech II - 2022; project No. MUNI/A/1137/2021) and by the Czech Science Foundation (GA CR), project No. GA22-27800S.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
You can try out the rule-based commas detection and correction at http://opravidlo.cz/.
- 2.
References
Pravidla českého pravopisu, 2. rozšířené vydání. Academia, Praha (1993)
Boháč, M., Rott, M., Kovář, V.: Text punctuation: an inter-annotator agreement study. In: Ekštein, K., Matoušek, V. (eds.) TSD 2017. LNCS (LNAI), vol. 10415, pp. 120–128. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64206-2_14
Bryant, C., Felice, M., Andersen, Ø.E., Briscoe, T.: The BEA-2019 shared task on grammatical error correction. In: Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 52–75. Association for Computational Linguistics, Florence, Italy (Aug 2019)
Cai, Y., Wang, D.: Question mark prediction by bert. In: 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 363–367 (2019). https://doi.org/10.1109/APSIPAASC47483.2019.9023090
Chordia, V.: PunKtuator: a multilingual punctuation restoration system for spoken and written text. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pp. 312–320. Association for Computational Linguistics, Online (Apr 2021). https://doi.org/10.18653/v1/2021.eacl-demos.37, https://aclanthology.org/2021.eacl-demos.37
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805 (2018). http://arxiv.org/abs/1810.04805
Hlaváčková, D., et al.: New online proofreader for Czech. Slavonic Natural Language Processing in the 21st Century, pp. 79–92 (2019)
Hlaváčková, D., Žižková, H., Dvořáková, K., Pravdová, M.: Develo** online czech proofreader tool: Achievements, limitations and pitfalls. In: Bohemistyka, XXII, (1), pp. 122–134 (2022). https://doi.org/10.14746/bo.2022.1.7
Hlubík, P., Španěl, M., Boháč, M., Weingartová, L.: Inserting punctuation to ASR output in a real-time production environment. In: Sojka, P., Kopeček, I., Pala, K., Horák, A. (eds.) TSD 2020. LNCS (LNAI), vol. 12284, pp. 418–425. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58323-1_45
Karlík, P., Nekula, M., Pleskalová, J.e.: Nový encyklopedický slovník češtiny (2012–2020). https://www.czechency.org/
Kilgarriff, A., Baisa, V., Bušta, J., Jakubíček, M., Kovář, V., Michelfeit, J., Rychlý, P., Suchomel, V.: The Sketch Engine: ten years on. Lexicography 1(1), 7–36 (2014). https://doi.org/10.1007/s40607-014-0009-9
Klejch, O., Bell, P., Renals, S.: Sequence-to-sequence models for punctuated transcription combining lexical and acoustic features. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 5700–5704 (2017). https://doi.org/10.1109/ICASSP.2017.7953248
Kovář, V., Machura, J., Zemková, K., Rott, M.: Evaluation and improvements in punctuation detection for czech. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2016. LNCS (LNAI), vol. 9924, pp. 287–294. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45510-5_33
Kovář, V., Horák, A., Jakubíček, M.: Syntactic analysis using finite patterns: a new parsing system for Czech. In: Vetulani, Z. (ed.) LTC 2009. LNCS (LNAI), vol. 6562, pp. 161–171. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20095-3_15
Liu, Y., et al.: Roberta: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692 (2019). http://arxiv.org/abs/1907.11692
Machura, J., Gerzová, H., Masopustová, M., Valícková, M.: Comparing majka and morphodita for automatic grammar checking. In: Proceedings of Recent Advances in Slavonic Natural Language Processing, RASLAN, pp. 3–14. Brno (2019)
Nunberg, G.: The Linguistics of Punctuation. CSLI lecture notes, Cambridge University Press (1990). https://books.google.cz/books?id=Sh-sruuKjJwC
Păiş, V., Tufiş, D.: Capitalization and punctuation restoration: a survey. Artif. Intell. Rev. 55(3), 1681–1722 (2021). https://doi.org/10.1007/s10462-021-10051-x
Pravdová, M., Svobodová, I.: Akademická příručka českého jazyka. Academia, Praha (2019)
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019)
Straková, J., Straka, M., Hajič, J.: Open-source tools for morphology, lemmatization, POS tagging and named entity recognition. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations. pp. 13–18. Association for Computational Linguistics, Baltimore, Maryland (Jun 2014). https://doi.org/10.3115/v1/P14-5003,https://aclanthology.org/P14-5003
Suchomel, V., Michelfeit, J., Pomikálek, J.: Text tokenisation using Unitok. In: Eight Workshop on Recent Advances in Slavonic Natural Language Processing, pp. 71–75. Tribun EU, Brno (2014). https://nlp.fi.muni.cz/raslan/2014/14.pdf
Švec, J., Lehečka, J., Šmídl, L., Ircing, P.: Transformer-based automatic punctuation prediction and word casing reconstruction of the ASR output. In: Ekštein, K., Pártl, F., Konopík, M. (eds.) TSD 2021. LNCS (LNAI), vol. 12848, pp. 86–94. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-83527-9_7
Švec, J., et al.: General framework for mining, processing and storing large amounts of electronic texts for language modeling purposes. Lang. Resour. Eval. 48(2), 227–248 (2014). https://doi.org/10.1007/s10579-013-9246-z
Šmerk, P.: Unsupervised learning of rules for morphological disambiguation. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2004. LNCS (LNAI), vol. 3206, pp. 211–216. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30120-2_27
Šmerk, P.: Fast morphological analysis of Czech. In: Proceedings of the RASLAN Workshop 2009. Masarykova univerzita, Brno (2009). https://nlp.fi.muni.cz/raslan/2009/papers/13.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Machura, J., Frémund, A., Švec, J. (2022). Automatic Grammar Correction of Commas in Czech Written Texts: Comparative Study. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2022. Lecture Notes in Computer Science(), vol 13502. Springer, Cham. https://doi.org/10.1007/978-3-031-16270-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-16270-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16269-5
Online ISBN: 978-3-031-16270-1
eBook Packages: Computer ScienceComputer Science (R0)