Stack Exchange Tagger
This work addresses the need for accurate taggers in large-scale text datasets like Stack Exchange, but it is incremental as it applies existing methods to a specific domain.
The authors tackled the problem of multilabel tag classification for Stack Exchange questions using only the title and text, finding that linear Support Vector Classification with the Crammer Singer technique yielded the best results.
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.