  The goal of our project is to develop an accurate tagger for questions posted
on Stack Exchange. Our problem is an instance of the more general problem of
developing accurate classifiers for large scale text datasets. We are tackling
the multilabel classification problem where each item (in this case, question)
can belong to multiple classes (in this case, tags). We are predicting the tags
(or keywords) for a particular Stack Exchange post given only the question text
and the title of the post. In the process, we compare the performance of
Support Vector Classification (SVC) for different kernel functions, loss
function, etc. We found linear SVC with Crammer Singer technique produces best
results.
