Adaptive Voting in Multiple Classifier Systems for Word Level Language Identification

In social media communication, code switching has become quite a common phenomenon especially for multilingual speakers. Automatic language identification becomes both a necessary and challenging task in such an environment. In this work, we describe a CRF based system with voting approach for code-mixed query word labeling at word-level as part of our participation in the shared task on Mixed Script Information Retrieval at Forum for Information Retrieval Evaluation (FIRE) in 2015. Our method uses character n-gram, simple lexical features and special character features, and therefore, can easily be replicated across languages. The performance of the system was evaluated against the test sets provided by the FIRE 2015 shared task on mixed script information retrieval. Experimental results show encouraging performance across the language pairs. CCS Concepts •Computer systems organization → Embedded systems; Redundancy; Robotics; •Networks → Network reliability;