NLP-NITMZ @ MSIR 2016 System for Code-Mixed Cross-Script Question Classification

This paper describes our approach on Code–Mixed Cross– Script Question Classification task, which is a subtask 1 of MSIR 2016. MSIR is a Mixed Script Information Retrieval event in conjunction with FIRE 2016, which is the 8th meeting of Forum for Information Retrieval Evaluation. For this task, our team NLP–NITMZ submitted three system runs such as: i) using a direct feature set; ii) using direct and dependent feature set and iii) using Naive Bayes classifier. The first system is our baseline system, which is based direct feature sets and we used a group of keywords to generate this direct feature set. To identify question classes our baseline system falls in ambiguity (means one question is tagged with multiple classes). To deal with this ambiguity, we developed another set of feature and we consider this feature set as dependent feature set, because keywords from this set is worked with direct feature set. The highest accuracy of our system is 78.88% using method–2 and we submitted as run–3. Our other two runs have same accuracy as 74.44%.