Code Mixed Cross Script Question Classification

With the growth in our society, one of the most affected aspect of our routine life is language. We tend to mix our conversations in more than one language, often mixing up regional language with English language is a lot more common practice. This mixing of languages is referred as code mixing, where we mix different linguistic constituents such as phrases, proper nouns, morphemes etc. to come up code mixed script. With exponential growth of social media, we are using more and more code mixed cross script for our conversation on Facebook, WhatsApp, or Twitter. On the other hand, the language should be understood by the automated question answering system which is one of the most import application of AI. And now the trend is code mixed languages but current work is around a single language. At FIRE 2016, as a part of Shared Task1 CMCS (Code Mixed Cross Script Question Classification), we have worked on the problem of classify a code mixed question into 9 given classes. Shared Task is focused on Indian regional languages, wherein we worked on BengaliEnglish code mixed cross script questions classification. As scripting used in training data is English only, so all Bengali text was also written using English script only. We have used Machine Learning for question classification and used ensemble based Random Forest algorithm. As it’s a code-mixed script, so traditional NLP components may not work well, so worked on a custom solution using own set of features for Classification. CCS Concepts • Theory of computation Random Forest • Computing methodologiesNatural language Processing