Normalization of Transliterated Words in Code-Mixed Data Using Seq2Seq Model & Levenshtein Distance
Soumil Mandal, Karthick Nanmaran · 2018
Building tools for code-mixed data is rapidly gaining popularity in the NLP research community as such data is exponentially rising on social media.Working with code-mixed data contains several challenges, especially due to grammatical inconsistencies and spelling variations in addition to all the previous known challenges for social media scenarios.In this article, we present a novel architecture focusing on normalizing phonetic typing variations, which is commonly seen in code-mixed data.One of the main features of our architecture is that in addition to normalizing, it can also be utilized for back-transliteration and word identification in some cases.Our model achieved an accuracy of 90.27% on the test data.