  The conventional natural language processing approaches are not accustomed to
the social media text due to colloquial discourse and non-homogeneous
characteristics. Significantly, the language identification in a multilingual
document is ascertained to be a preceding subtask in several information
extraction applications such as information retrieval, named entity
recognition, relation extraction, etc. The problem is often more challenging in
code-mixed documents wherein foreign languages words are drawn into base
language while framing the text. The word embeddings are powerful language
modeling tools for representation of text documents useful in obtaining
similarity between words or documents. We present a simple probabilistic
approach for building efficient word embedding for code-mixed text and
exemplifying it over language identification of Hindi-English short test
messages scrapped from Twitter. We examine its efficacy for the classification
task using bidirectional LSTMs and SVMs and observe its improved scores over
various existing code-mixed embeddings
