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WASSUP? LOL : Characterizing Out-of-Vocabulary Words in Twitter

Language in social media is mostly driven by new words and spellings that are constantly entering the lexicon thereby polluting it and resulting in high deviation from the formal written version. The primary entities of such language are the out-of-vocabulary (OOV) words. In this paper, we study various sociolinguistic properties of the OOV words and propose a classification model to categorize them into at least six categories. We achieve 81.26% accuracy with high precision and recall. We observe that the content features are the most discriminative ones followed by lexical and context features.

preprint2016arXivOpen access
Suman Kalyan MaityChaitanya SardaAnshit ChaudharyAbhijeet PatilShraman KumarAkash MondalAnimesh Mukherjee
Computation and LanguageSocial and Information Networks
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Open access7 authors2 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Suman Kalyan MaityChaitanya SardaAnshit ChaudharyAbhijeet PatilShraman KumarAkash MondalAnimesh Mukherjee

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