%0 Conference Proceedings %T All that is English may be Hindi: Enhancing language identification through automatic ranking of the likeliness of word borrowing in social media %A Patro, Jasabanta %A Samanta, Bidisha %A Singh, Saurabh %A Basu, Abhipsa %A Mukherjee, Prithwish %A Choudhury, Monojit %A Mukherjee, Animesh %Y Palmer, Martha %Y Hwa, Rebecca %Y Riedel, Sebastian %S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing %D 2017 %8 September %I Association for Computational Linguistics %C Copenhagen, Denmark %F patro-etal-2017-english %X n 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. %R 10.18653/v1/D17-1240 %U https://aclanthology.org/D17-1240 %U https://doi.org/10.18653/v1/D17-1240 %P 2264-2274