All that is English may be Hindi: Enhancing language identification through automatic ranking of likeliness of word borrowing in social media
Table 8: Spearman’s correlation between UUR and the ground truth in the different user buckets. Best results are marked in bold.
['Bucket', 'Number of users', '[ITALIC] ρ']
As mentioned earlier, we divide the set of 3577 users into three categories. As we can see, for Low bucket the ρ value is maximum. This points to the fact that the signals of borrowing is strongest from the users who rarely mix languages.