Advantages of online spellchecking: a Croatian example

Dembitz, Šandor; Randić, Mirko; Gledec, Gordan

doi:10.1002/spe.1037

Cited by 5 publications

(5 citation statements)

References 22 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The entered text is stripped of all markup tags, formatting, and most punctuation, sent to the server via an Ajax call, and processed by the spellchecking application [20]. The inner workings of the spellchecker are described in [2,8,20,21]. 3.…”

Section: Data Collectionmentioning

confidence: 99%

See 1 more Smart Citation

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

Gledec

Horvat

Mikuc

et al. 2023

Data

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Data Collectionmentioning

confidence: 99%

“…Spellchecking services play a crucial role in ensuring the quality and readability of digital content, aiding both native speakers and language learners in producing error-free text [1]. This is particularly crucial for languages such as Croatian, which exhibit complex morphological and orthographic rules [2].…”

Section: Introductionmentioning

confidence: 99%

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

Gledec

Horvat

Mikuc

et al. 2023

Data

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hascheck's architecture is explained in detail in [21]. Here we give only the size of our dictionary, which has changed significantly since [21] was published. The dictionary is organized into three word-list files that contained as of 1 June 2019:…”

Section: About the Spellcheckermentioning

confidence: 99%

“…Unigrams are also subject to a learning process. Hascheck's word-guessing algorithm, described in [21], suggests to human supervisors which tokens are worth considering as potential new word-or name-types. After checking them in the context they appear in, the supervisor makes a final decision.…”

Section: A Hapax Legomenamentioning

confidence: 99%

Dynamic N-Gram System Based on an Online Croatian Spellchecking Service

2019

Self Cite

View full text Add to dashboard Cite

As an infrastructure able to accelerate the development of natural language processing applications, large-scale lexical n-gram databases are at present important data systems. However, deriving such systems for world minority languages as it was done in the Google n-gram project leads to many obstacles. This paper presents an innovative approach to large-scale n-gram system creation applied to the Croatian language. Instead of using the Web as the world's largest text repository, our process of n-gram collection relies on the Croatian online academic spellchecker Hascheck, a language service publicly available since 1993 and popular worldwide. Our n-gram filtering is based on dictionary criteria, contrary to the publicly available Google n-gram systems in which cutoff criteria were applied. After 12 years of collecting, the size of the Croatian n-gram system reached the size of the largest Google Version 1 n-gram systems. Due to reliance on a service in constant use, the Croatian n-gram system is a dynamic one. System dynamics allowed modeling of n-gram count behavior through Heaps' law, which led to interesting results. Like many minority languages, the Croatian language suffers from a lack of sophisticated language processing systems in many application areas. The importance of a rich lexical n-gram infrastructure for rapid breakthroughs in new application areas is also exemplified in the paper.

show abstract

“…the improvement of the spellchecker's functionality. The learning system incorporated into the post-processing subsystem is what makes Hascheck different from other spellcheckers 7 .…”

Section: Croatian Big Data Toolmentioning

confidence: 99%

An Economic Approach to Big Data in a Minority Language

Dembitz

Gledec

Sokele³

2014

Procedia Computer Science

Self Cite

View full text Add to dashboard Cite

Google's n-gram project brought recently big data benefits to several main world languages, like English, Chinese etc. Any attempt to derive such systems, aimed to accelerate the development of NLP applications for world minority languages, in the manner in which it has been done in the project, encounters many obstacles. This paper presents an innovative and economic approach to large-scale n-gram system creation applied to the Croatian language case. Instead of using the Web as the world's biggest text repository, our process of n-gram collection relies on the Croatian academic online spellchecker Hascheck, a language service publicly available since 1993 and popular worldwide. The service has already processed a corpus whose size exceeds the size of the Croatian web-corpus created in recent years. Contrary to the Google n-gram systems, where cutoff criteria were applied, our n-gram filtering is based on dictionary criteria. This resulted in a system comparable in size to the largest ngram systems of today. Because of the reliance on a service in constant use, the Croatian n-gram system is a dynamic one, unique among the systems compared. The importance of having an n-gram infrastructure for rapid breakthroughs in new application areas is also exemplified in the paper.

show abstract

Advantages of online spellchecking: a Croatian example

Cited by 5 publications

References 22 publications

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

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

Dynamic N-Gram System Based on an Online Croatian Spellchecking Service

An Economic Approach to Big Data in a Minority Language

Contact Info

Product

Resources

About