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In this paper, we present a set of computational methods to identify thelikeliness of a word being borrowed, based on the signals from social media. Interms of Spearman correlation coefficient values, our methods perform more thantwo times better (nearly 0.62) in predicting the borrowing likeliness comparedto the best performing baseline (nearly 0.26) reported in literature. Based onthis likeliness estimate we asked annotators to re-annotate the language tagsof foreign words in predominantly native contexts. In 88 percent of cases theannotators felt that the foreign language tag should be replaced by nativelanguage tag, thus indicating a huge scope for improvement of automaticlanguage identification systems.

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