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Identifying and Analyzing Different Aspects of English-Hindi Code-Switching in Twitter

机译:识别和分析Twitter中英语-印地语代码转换的不同方面

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摘要

Code-switching or the juxtaposition of linguistic units from two or more languages in a single utterance, has, in recent times, become very common in text, thanks to social media and other computer mediated forms of communication. In this exploratory study of English-Hindi code-switching on Twitter, we automatically create a large corpus of code-switched tweets and devise techniques to identify the relationship between successive components in a code-switched tweet. More specifically, we identify pragmatic functions such as narrative-evaluative, negative reinforcement, translation or semantically equivalent statements, and so on characterizing the relation between successive components. We analyze the difference/similarity between switching patterns in code-switched and monolingual multi-component tweets. We observe strong dominance of narrative-evaluative (non-opinion to opinion or vice versa) switching in case of both code-switched and monolingual multi-component tweets in around 40% of cases. Polarity switching appears to be a prevalent switching phenomenon (10%) specifically in code-switched tweets (three to four times higher than monolingual multi-component tweets) where preference of expressing negative sentiment in Hindi is approximately twice compared to English. Positive reinforcement appears to be an important pragmatic function for English multi-component tweets, whereas negative reinforcement plays a key role for Devanagari multi-component tweets. Our results also indicate that the extent and nature of code-switching also strongly depend on the topic (sports, politics, etc.) of discussion.
机译:近年来,由于社交媒体和其他计算机介导的交流形式,代码转换或两种语言或多种语言的语言单元并置成为一种非常普遍的文本。在Twitter上的英语-印地语代码转换探索性研究中,我们自动创建了一个大型的代码转换推文集,并设计了一些技术来识别代码转换推文中各连续组件之间的关系。更具体地说,我们确定了实用功能,例如叙事评估,否定强化,翻译或语义等效的陈述等,以表征连续成分之间的关​​系。我们分析了代码交换和单语言多分量推文中的交换模式之间的差异/相似性。我们发现,在大约40%的案例中,在代码转换和单语多分量推文的情况下,叙事评估(意见不反对,反之亦然)切换的优势明显。极性切换似乎是一种普遍的切换现象(10%),特别是在代码切换的推文中(比单语多分量推文高三到四倍),在北印度语中表达负面情绪的偏好大约是英语的两倍。正增强似乎是英语多分量推文的重要实用功能,而负增强对于梵文多分量推文起着关键作用。我们的结果还表明,代码切换的范围和性质也强烈取决于讨论的主题(体育,政治等)。

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