Cyber socialising: Emerging genres and registers of intimacy among young South African students

Bock, Zannie

doi:10.1080/10228195.2013.784924

Cited by 30 publications

(8 citation statements)

References 10 publications

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“…The author observed that most often mixing takes place at the beginning of a sentence as well as through simple insertions. Similar observations about chat and email messages have been reported in (Bock, 2013;Negrón, 2009). However, studies of code-mixing with Chinese-English bilinguals from Hong Kong (Li, 2009) and Macao (San, 2009) brings forth results that contrast the aforementioned findings and indicate that in these societies code-mixing is driven more by linguistic than social motivations.…”

Section: Related Worksupporting

confidence: 87%

All that is English may be Hindi: Enhancing language identification through automatic ranking of the likeliness of word borrowing in social media

Patro¹,

Samanta²,

Singh³

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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In this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman's correlation values, our methods perform more than two times better (∼ 0.62) in predicting the borrowing likeliness compared to the best performing baseline (∼ 0.26) reported in literature. Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts. In 88% of cases the annotators felt that the foreign language tag should be replaced by native language tag, thus indicating a huge scope for improvement of automatic language identification systems.

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Section: Related Worksupporting

confidence: 87%

All that is English may be Hindi: Enhancing language identification through automatic ranking of the likeliness of word borrowing in social media

Patro¹,

Samanta²,

Singh³

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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show abstract

“…The author observed that most often mixing takes place at the beginning of a sentence as well as through simple insertions. Similar observations about chat messages have been reported in (Bock 2013). (Negrón 2009) reported a study on code-mixing in the emails of five Spanish-English bilinguals and reiterated similar results as in the case of SMS and chat messages.…”

Section: Related Worksupporting

confidence: 82%

Is this word borrowed? An automatic approach to quantify the likeliness of borrowing in social media

Patro,

Samanta,

Singh

et al. 2017

Preprint

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Code-mixing or code-switching refer to the phenomenon of effortless and natural switching between two or more languages in a single conversation, sometimes even in a single utterance, by multilingual speakers. However, use a foreign word in a language does not necessarily mean that the speaker is code-switching, because often languages borrow lexical items from other languages. If a word is borrowed, it becomes a part of the lexicon of a language; whereas, during code-switching the speaker is aware that the conversation involves multiple languages and often the switching is intentional. Identifying whether a non-native word used by a bilingual speaker is due to borrowing or code-switching is not only of fundamental importance to theories of multilingualism, but it is also an essential prerequisite towards development of language and speech technologies for multilingual communities. In this paper, we present for the first time, a series of computational methods to identify the likeliness of a word being borrowed or code-mixed, based on the signals from social media. In particular, we use tweets from English-Hindi bilinguals from India to predict word borrowing. We first propose a method to sample a set of candidate words from the social media data using a context based clustering approach. Next, we propose three novel and similar metrics based on the usage of these words by the users in different tweets; we then apply these metrics to score and rank the candidate words indicating their likeliness of being borrowed. We compare these rankings with a ground truth ranking constructed through a human judgement experiment. The Spearman's rank correlation between the two rankings (∼ 0.62 for all the three metric variants) is more than double the value (0.26) of the most competitive existing baseline reported in the literature. Some other striking observations are -(i) the correlation is higher for the ground truth data elicited from the younger participants (age < 30) than that from the older participants; since language change is brought about by the younger generation, this possibly indicates that social media is able to provide very early signals of borrowing, and (ii) those participants who use mixed-language for tweeting the least, provide the best signals of borrowing.

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“…In a multilingual environment, different social media phenomena such as lexical borrowing, code-mixing, and phonetic typing lead to shared vocabulary across discourse in different languages. While several studies (Bock (2013); Yip and Matthews (2016)) focus on the sociological and linguistic aspects of these phenomena, from the language identification standpoint, not much effort has been directed towards understanding the diversified nature of vocabulary in multilingual social media text and its impact on language identification. In particular, the examples discussed above highlight two major challenges for language identification in a multilingual environment.…”

Section: Introductionmentioning

confidence: 99%

SwitchNet: Learning to switch for word-level language identification in code-mixed social media text

2021

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Word-level language identification is an essential prerequisite for extracting useful information from code-mixed social media content. Previous studies in word-level language identification show two important observations. First, the local context is an important indicator of the language of a word when a word is valid in multiple languages. Second, considering the word in isolation from its context leads to more effective language classification when a word is borrowed or embedded into sentences of other languages. In this paper, we propose a framework for language identification that makes use of a dynamic switching mechanism for effective language classification of both words that are borrowed or embedded from other languages as well as words that are valid in multiple languages. For a given input, the proposed switching mechanism makes a dynamic decision to bias its prediction either towards the prediction obtained by the contextual information or that obtained by the word in isolation. In contrast to existing studies that rely upon large amounts of annotated data for robust performance in a multilingual environment, the proposed approach uses minimal annotated resources and no external resources, making it easily extendible to newer languages. Evaluation over a corpus of transliterated Facebook comments shows that the proposed approach outperforms its baseline counterparts: classification based on the contextual information, classification based on the word in isolation, as well as an ensemble of the two classifiers.

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Cyber socialising: Emerging genres and registers of intimacy among young South African students

Cited by 30 publications

References 10 publications

All that is English may be Hindi: Enhancing language identification through automatic ranking of the likeliness of word borrowing in social media

All that is English may be Hindi: Enhancing language identification through automatic ranking of the likeliness of word borrowing in social media

Is this word borrowed? An automatic approach to quantify the likeliness of borrowing in social media

SwitchNet: Learning to switch for word-level language identification in code-mixed social media text

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