Abstract
Spelling errors, a commonplace phenomenon among students dominated by those with learning disabilities, call for effective systems that automatically and accurately identify and classify spelling mistakes. This research focused on developing an automatic spelling error classification tool to identify the Malayalam writing system’s phonological and orthographic spelling error categories. For the analysis of spelling errors in Malayalam, the study considered the input pair of real words and spelled words for extraction of six linguistic features: the difference in word length, edit distance, character overlapping percentage, the number of common phonemes, the number of common bigrams, and the number of common trigrams. After analysing spelling errors, machine learning techniques classify them. The research appraised the effectiveness of feature engineering apropos automatic spelling error classification in Malayalam. Results indicate that the fine-tuned Random Forest classifier achieved the highest classification accuracy of 71%, effectively distinguishing between the two error categories. The findings pinpoint the impact of linguistic features on accuracy and afford insights into the imperative of language technology tools for educational purposes.
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
References
Bright W (1999) A matter of typology: alphasyllabaries and abugidas. In: WRIT—1133ten Lang. literacy, vol 2, no 1, pp. 45–55, 1999. Available 1134. https://www.jbeplatform.com/content/journals/10.1075/wll.2.1.03bri
Haridas M, Vasudevan N, Nair GJ, Gutjahr G, Raman R, Nedungadi P (2018) Spelling errors by normal and poor readers in a bilingual Malayalam-English dyslexia screening test. In: 2018 IEEE 18th international conference on advanced learning technologies (ICALT), pp 340–344. https://doi.org/10.1109/ICALT.2018.00085
Gafoor K, Remia K (2013) Spelling difficulties in Malayalam among lower primary students, vol 5, 31–40
Haridas M, Vasudevan N, Iyer, A, Menon R, Nedungadi P (2017) Analyzing the responses of primary school children in dyslexia screening tests. In: Proceedings of the IEEE 5th international conference on MOOCs, innovation and technology in education (MITE), pp 89–94. https://doi.org/10.1109/MITE.2017.00022
Haridas M, Vasudevan N, Gutjahr G, Raman R, Nedungadi P (2020) Comparing English and Malayalam spelling errors of children using a bilingual screening tool. In: Advances in intelligent systems and computing, vol 1027. Springer, pp 427–436. https://doi.org/10.1007/978-981-32-9343-4_34
Sooraj SK, Manjusha, Kumar M, Kp S (2018) Deep learning based spell checker for Malayalam language. J Intell Fuzzy Syst 34:1427–1434. https://doi.org/10.3233/JIFS-169438
Premjith B, Soman KP, Kumar MA (2018) A deep learning approach for Malayalam morphological analysis at character level. Procedia Comput Sci 132:47–54. https://doi.org/10.1016/j.procs.2018.05.058
Berkling J, Lavalley R (2018) Automatic orthographic error tagging and classification for German texts. Compu Speech Lang 52:56–78. ISSN 0885-2308. https://doi.org/10.1016/j.csl.2017.11.002
Protopapas A, Fakou A, Drakopoulou S, Skaloumbakas C, Mouzaki A (2012) What do spelling errors tell us? Classification and analysis of errors made by Greek schoolchildren with and without dyslexia. Reading Writing 26. https://doi.org/10.1007/s11145-012-9378-3
Vasudevan N, Haridas M, Nedungadi P, Raman R, Daniels PT, Share DL(2023) A multi-dimensional framework for characterising the role of writing system variation in literacy learning: a case study in Malayalam. Reading Writing https://doi.org/10.1007/s11145-022-10374-3
Renjit S, Idicula SM (2022) Feature based entailment recognition for Malayalam language texts. Int J Adv Comput Sci Appl 13(2). https://doi.org/10.14569/IJACSA.2022.0130283 (The Science and Information Organization)
García-Díaz JA, Vivancos-Vicente PJ, Almela Á, Valencia-García R (2022) UMUTextStats: a linguistic feature extraction tool for Spanish. In: Proceedings of the thirteenth language resources and evaluation conference, June 2022, Marseille, France. European Language Resources Association, pp 6035–6044. https://aclanthology.org/2022.lrec-1.649
Pennebaker J, Francis M, Booth R (1999) Linguistic inquiry and word count (LIWC)
Spoon K, Crandall D, Siek K (2010) Towards detecting dyslexia in children’s handwriting using neural networks. In: Proceedings of the international conference on machine learning AI for social good workshop, pp 1–5
Spoon K, Siek K, Crandall D, Fillmore M (2019) Can we (and should we) use AI to detect dyslexia in children’s handwriting?. In: Proceedings of the international conference on machine learning AI for social good workshop, Long Beach, CA, USA, 9–15 June 2019, pp 1–6
Christopher B, Mariano F, Ted B (2017) Automatic annotation and evaluation of error types for grammatical error correction. 1:793–805. https://doi.org/10.18653/V1/P17-1074
Korre K, Chatzipanagiotou M, Pavlopoulos J (2021) ELERRANT: automatic grammatical error type classification for greek. 708–717. https://doi.org/10.26615/978-954-452-072-4_081
Suhaidi M, Abdul Kadir R, Tiun S (2021) A review of feature extraction methods on machine learning. J Inf Syst Technol Manage
Manohar K, Jayan A, Rajan R (2022) Mlphon: a multifunctional grapheme-phoneme conversion tool using finite state transducers. IEEE Access 1–1. https://doi.org/10.1109/ACCESS.2022.3204403
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dhanya, S., Kaimal, M.R., Nedungadi, P. (2024). Automatic Spelling Error Classification in Malayalam. In: Joshi, A., Mahmud, M., Ragel, R.G., Kartik, S. (eds) ICT: Cyber Security and Applications. ICTCS 2022. Lecture Notes in Networks and Systems, vol 916. Springer, Singapore. https://doi.org/10.1007/978-981-97-0744-7_25
Download citation
DOI: https://doi.org/10.1007/978-981-97-0744-7_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0743-0
Online ISBN: 978-981-97-0744-7
eBook Packages: EngineeringEngineering (R0)