Architecture of Hascheck – An Intelligent Spellchecker for Croatian Language

Dembitz, Šandor; Gledec, Gordan; Blaskovic, B.

doi:10.1007/978-3-642-15390-7_30

Cited by 3 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The architecture of the Croatian Academic Spelling Checker (Croatian: "Hrvatski akademski spelling checker", abbreviated as "Hascheck" and pronounced as "Hašek", as it was known for more than 20 years) is extensively described in [37].…”

Section: Ispravime-croatian Online Spellcheckermentioning

confidence: 99%

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

Gledec,

Sokele,

Horvat

et al. 2024

Computers

Self Cite

View full text Add to dashboard Cite

This paper introduces a novel approach to the creation and application of confusion matrices for error pattern discovery in spellchecking for the Croatian language. The experimental dataset has been derived from a corpus of mistyped words and user corrections collected since 2008 using the Croatian spellchecker available at ispravi.me. The important role of confusion matrices in enhancing the precision of spellcheckers, particularly within the diverse linguistic context of the Croatian language, is investigated. Common causes of spelling errors, emphasizing the challenges posed by diacritic usage, have been identified and analyzed. This research contributes to the advancement of spellchecking technologies and provides a more comprehensive understanding of linguistic details, particularly in languages with diacritic-rich orthographies, like Croatian. The presented user-data-driven approach demonstrates the potential for custom spellchecking solutions, especially considering the ever-changing dynamics of language use in digital communication.

show abstract

Section: Ispravime-croatian Online Spellcheckermentioning

confidence: 99%

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

Gledec,

Sokele,

Horvat

et al. 2024

Computers

Self Cite

View full text Add to dashboard Cite

show abstract

“…Preprocessing on n-grams was accomplished during the spellchecking process and n-grams were stored in a text file [81,86,87]. Using the tokenization and lemmatization functions of the CLASSLA library, the n-grams were processed, and lemmatized words were saved as the final result.…”

Section: N-grams Preprocessingmentioning

confidence: 99%

Hybrid Natural Language Processing Model for Sentiment Analysis during Natural Crisis

Horvat,

Gledec,

Leontić

2024

Electronics

Self Cite

View full text Add to dashboard Cite

This paper introduces a novel natural language processing (NLP) model as an original approach to sentiment analysis, with a focus on understanding emotional responses during major disasters or conflicts. The model was created specifically for Croatian and is based on unigrams, but it can be used with any language that supports the n-gram model and expanded to multiple word sequences. The presented model generates a sentiment score aligned with discrete and dimensional emotion models, reliability metrics, and individual word scores using affective datasets Extended ANEW and NRC WordEmotion Association Lexicon. The sentiment analysis model incorporates different methodologies, including lexicon-based, machine learning, and hybrid approaches. The process of preprocessing includes translation, lemmatization, and data refinement, utilized automated translation services as well as the CLARIN Knowledge Centre for South Slavic languages (CLASSLA) library, with a particular emphasis on diacritical mark correction and tokenization. The presented model was experimentally evaluated on three simultaneous major natural crises that recently affected Croatia. The study’s findings reveal a significant shift in emotional dimensions during the COVID-19 pandemic, particularly a decrease in valence, arousal, and dominance, which corresponded with the two-month recovery period. Furthermore, the 2020 Croatian earthquakes elicited a wide range of negative discrete emotions, including anger, fear, and sadness, with the recuperation period much longer than in the case of COVID-19. This study represents an advancement in sentiment analysis, particularly in linguistically specific contexts, and provides insights into the emotional landscape shaped by major societal events.

show abstract

“…2. 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].…”

Section: Data Collectionmentioning

confidence: 99%

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

Gledec

Horvat

Mikuc

et al. 2023

Data

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

Architecture of Hascheck – An Intelligent Spellchecker for Croatian Language

Cited by 3 publications

References 12 publications

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

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

Hybrid Natural Language Processing Model for Sentiment Analysis during Natural Crisis

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

Contact Info

Product

Resources

About