Normalization of Transliterated Words in Code-Mixed Data Using Seq2Seq Model & Levenshtein Distance
This addresses challenges in NLP for social media users dealing with code-mixed data, but it is incremental as it builds on existing methods.
The paper tackled the problem of normalizing phonetic typing variations in code-mixed data, achieving an accuracy of 90.27% on test data.
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.