C. Anton Rytting
C. Anton Rytting has 3 papers in our library, in 2010–2018, most of them on Semitic languages; often with Paul Rodrigues, Tim Buckwalter, Valerie Novak.
OpenAlex · ORCID · Wikidata · h-index 6 · 32 works · 135 citations OpenAlex, 2026-10-14
📄 3 works in our library · 🔗 Cited 3 times in our library · ⭐ Paper Stars ranking: #9263
Affiliations
🎓University of Maryland, College ParkCollege Park ·
United States2007–2026 · 13 papersEmployment 2007–2026 · Associate Research Scientist🔬Applied Research Laboratory for Intelligence and SecurityCollege Park2023–2025 · 4 papersEmployment🎓The Ohio State UniversityColumbus ·
United States2000–2010 · 9 papersEducation 2000–2007 · Ph.D., Linguistics🎓Brigham Young UniversityProvo ·
United States1994–2000Education 1994–2000 · B. A., Linguistics
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksEducation · 1 workSocial Sciences · 1 work
🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 3 works
Works by year
2010s
Papers
- Error Correction for Arabic Dictionary Lookup (2010 · 7 citations)
Learners of Arabic often struggle to find words in electronic dictionaries. They mix up letters that look alike, miss sound contrasts they cannot hear, and stumble over unfamiliar Romanization. Colloquial dialects make all of this harder. The authors built a spelling corrector for lookup in an Iraqi Arabic–English… - ArCADE: An Arabic Corpus of Auditory Dictation Errors (2014 · 6 citations)
Learner corpora for Arabic come almost entirely from written essays. Spelling slips that come from mishearing are rare in such data and hard to tell apart from other errors. Generic spell checkers also handle these errors poorly, because they are tuned to native-speaker mistakes. The authors built ArCADE, a… - Arabic Data Science Toolkit: An API for Arabic Language Feature Extraction (2018 · 4 citations)
The paper presents the Arabic Data Science Toolkit (ADST). It is meant for data scientists who need linguistically informed features from Arabic text but know neither Arabic nor NLP. The authors argue that purely statistical representations such as bag of words work poorly for a morphologically rich language like…
Co-authors here
Venues
- Language Resources and Evaluation Conference (LREC) ×2: LREC 2018, LREC 2010
- Workshop on Innovative Use of NLP for Building Educational Applications (BEA): BEA 2014
Cited by
Updated
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