A Comprehensive Dataset of Spelling Errors and Users’ Corrections in Croatian Language

Abstract

This paper presents a unique and extensive dataset containing over 33 million entries with pairs in the form “spelling error → correction” from ispravi.me, the most popular Croatian online spellchecking service, collected since 2008. The dataset, compiled from the contribution of nearly 900,000 users, is a valuable resource for researchers and developers in the field of natural language processing (NLP), improving spellcheck accuracy, and language learning applications. The dataset may be used to accomplish several goals: (1) improving spellchecking accuracy by incorporating common user corrections and reducing false positives and negatives; (2) helping language learners identify common errors and learn correct spelling through targeted feedback; (3) analyzing data trends and patterns to uncover the most common spelling errors and their underlying causes; (4) identifying and evaluating factors that influence typing input; (5) improving NLP applications such as text recognition and machine translation. Tasks specific to the Croatian language include the creation of a letter-level confusion matrix and the refinement of word suggestions based on historical usage of the service. This comprehensive dataset provides researchers and practitioners with a wealth of information, opening the path for advancements in spellchecking, language learning, and NLP applications in the Croatian language.

Error Pattern Discovery in Spellchecking Using Multi-Class Confusion Matrix Analysis for the Croatian Language

“…Each time users chose the proper correction candidate, the pair "error word → correct word" was logged on the server. That gave us a huge dataset, published in [36].…”

“…In [36] the authors presented an extensive dataset containing a total of 33,382,330 entries of the form "error word → correct word" collected between December 2008 and March 2023 compiled from the contributions of nearly 900,000 users of ispravi.me, the most popular Croatian online spellchecking service. In this huge dataset, the authors identified 5,584,226 unique "error word → correct word" pairs.…”

“…Using a subset of data from the authors' extensive dataset [36], three matrices for each type of error with the following values were created:…”

“…Explanations for both errors are given in Secsented as "@", as in [12], meaning the beginning or the end ause the character can be inserted or deleted at the begindatia" → "dodati" [to add]; psodij" → "rapsodija" [rhapsody]; ve those two characters to maintain matrices at 30x30 letinconsistencies since the total count of errors would not be d on the previous or the following letter. rices the authors extensive dataset [36], three matrices for each values were created: occurred; error on a given letter; error with respect to the whole analyzed subset. • deletionCondOnPrevious is convenient to track preposition instead of "s", as one would need which would include cases where "na" [on] wa "da" [yes] as "d, "ja" [I] as "j", etc.…”