=Paper=
{{Paper
|id=Vol-1917/paper08
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1917/paper08.pdf
|volume=Vol-1917
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1917/paper08.pdf</pdf>
<pre>
                    Predicting Tags for Stack Overflow Questions

                                   Sayan Pal, Ammar Shaker, and Eyke Hüllermeier

                                                  Intelligent Systems Group
                                                    Paderborn University

                     Stack Overflow (https://stackoverflow.com/) is one of the major community-
                 driven Question and Answer (Q&A) websites, focusing on topics related to
                 computer programming. It has nearly 7 million users, who ask more than 6700
                 questions every day. Each question can be associated with up to five different
                 tags, which serve as metadata to facilitate information retrieval.
                     In this paper, we consider the problem of supporting this process through
                 automatic tagging. From a machine learning point of view, we are facing a problem
                 of Extreme Multi-Label Classification (XMLC), as Stack Overflow allows for
                 choosing from several thousands of tags. Besides, instead of learning in a standard
                 batch mode, it is desirable to learn incrementally on the continuous stream of
                 questions entering the system, with the capability to capture changes and drifts
                 in the data; for example, many tags (such as ‘facebook’) have a lifetime, first
                 gaining popularity, then reaching a peak and eventually diminishing.
                     Thus, we end up with an extremely challenging problem of XMLC for data
                 streams with a non-stationary set of labels. To tackle this problem, we build on
                 an XMLC method based on probabilistic label trees (PLT), which has recently
                 been proposed in [1]. We extend this approach in two directions. First, instead
                 of specifying the entire PLT beforehand, we develop an adaptive version that
                 starts with only a single node and expands the tree whenever a new label is
                 observed in a training example. As a second contribution, we further improve
                 adaptive PLTs through stream-based boosting [2]. More specifically, we apply the
                 online boosting method by Oza and Russell, which we tailor for minimizing the
                 F-measure as a performance metric (instead of 0/1 loss, which is not appropriate
                 in the context of XMLC).
                     In addition to the methodological contributions, we present empirical results
                 based on extensive experiments with real data from Stack Overflow. Our ex-
                 perimental setting is focused on evaluating the usefulness of the extensions we
                 proposed for PLTs, i.e., the adaptive handling of labels and online boosting.

                 References
                 1. Kalina Jasinska, Krzysztof Dembczynski, Róbert Busa-Fekete, Karlson Pfannschmidt,
                    Timo Klerx, and Eyke Hüllermeier. Extreme F-measure maximization using sparse
                    probability estimates. In Proc. ICML 2016, New York City, NY, USA, pages
                    1435–1444, 2016.
                 2. Nikunj C. Oza and Stuart Russell. Online bagging and boosting. In In Artificial
                    Intelligence and Statistics 2001, pages 105–112. Morgan Kaufmann, 2001.
                 3. Gene Smith. Tagging : people-powered metadata for the social web. New Riders,
                    Berkeley, CA, 2008.




Copyright © 2017 by the paper’s authors. Copying permitted only for private and academic purposes.
In: M. Leyer (Ed.): Proceedings of the LWDA 2017 Workshops: KDML, FGWM, IR, and FGDB.
Rostock, Germany, 11.-13. September 2017, published at http://ceur-ws.org

</pre>