Sökning: id:"swepub:oai:gup.ub.gu.se/246849" >
Romanized Berber an...
Romanized Berber and Romanized Arabic Automatic Language Identification Using Machine Learning
-
- Adouane, Wafia, 1985 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för filosofi, lingvistik och vetenskapsteori,Department of Philosophy, Linguistics and Theory of Science
-
Semmar, Nasredine (författare)
-
- Johansson, Richard, 1975 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU)
-
(creator_code:org_t)
- Association for Computational Linguistics, 2016
- 2016
- Engelska.
-
Ingår i: Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; 53–61; December 12, 2016 ; Osaka, Japan. - : Association for Computational Linguistics. - 0736-587X.
- Relaterad länk:
-
https://gup.ub.gu.se...
Abstract
Ämnesord
Stäng
- The identification of the language of text/speech input is the first step to be able to properly do any language-dependent natural language processing. The task is called Automatic Language Identification (ALI). Being a well-studied field since early 1960’s, various methods have been applied to many standard languages. The ALI standard methods require datasets for training and use character/word-based n-gram models. However, social media and new technologies have contributed to the rise of informal and minority languages on the Web. The state-of-the-art automatic language identifiers fail to properly identify many of them. Romanized Arabic (RA) and Romanized Berber (RB) are cases of these informal languages which are under-resourced. The goal of this paper is twofold: detect RA and RB, at a document level, as separate languages and distinguish between them as they coexist in North Africa. We consider the task as a classification problem and use supervised machine learning to solve it. For both languages, character-based 5-grams combined with additional lexicons score the best, F-score of 99.75% and 97.77% for RB and RA respectively.
Ämnesord
- HUMANIORA -- Språk och litteratur -- Studier av enskilda språk (hsv//swe)
- HUMANITIES -- Languages and Literature -- Specific Languages (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
Nyckelord
- natural language processing
- Berber
- Arabic
- language classification
- machine learning
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Hitta via bibliotek
Till lärosätets databas