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        <title>An Assessment of Language Identification Methods on Tweets and Wikipedia Articles</title>
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    <abstract>Language identification is the task of determining the language which a given text is written. This task is important for Natural Language Processing and Information Retrieval activities. Two popular approaches for language identification are the N-grams and stopwords models. In this paper, these two models were tested on different types of documents such as short, irregular texts (tweets) and long, regular texts (Wikipedia articles).</abstract>
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    <identifier type="doi">10.18653/v1/2020.winlp-1.15</identifier>
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%0 Conference Proceedings
%T An Assessment of Language Identification Methods on Tweets and Wikipedia Articles
%A Vernetti, Pedro
%A Freitas, Larissa
%S Proceedings of the The Fourth Widening Natural Language Processing Workshop
%D 2020
%8 jul
%I Association for Computational Linguistics
%C Seattle, USA
%F vernetti-freitas-2020-assessment
%X Language identification is the task of determining the language which a given text is written. This task is important for Natural Language Processing and Information Retrieval activities. Two popular approaches for language identification are the N-grams and stopwords models. In this paper, these two models were tested on different types of documents such as short, irregular texts (tweets) and long, regular texts (Wikipedia articles).
%R 10.18653/v1/2020.winlp-1.15
%U https://aclanthology.org/2020.winlp-1.15
%U https://doi.org/10.18653/v1/2020.winlp-1.15
%P 58-60

Markdown (Informal)

[An Assessment of Language Identification Methods on Tweets and Wikipedia Articles](https://aclanthology.org/2020.winlp-1.15) (Vernetti & Freitas, WiNLP 2020)

• An Assessment of Language Identification Methods on Tweets and Wikipedia Articles (Vernetti & Freitas, WiNLP 2020)

ACL

• Pedro Vernetti and Larissa Freitas. 2020. An Assessment of Language Identification Methods on Tweets and Wikipedia Articles. In Proceedings of the The Fourth Widening Natural Language Processing Workshop, pages 58–60, Seattle, USA. Association for Computational Linguistics.