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A Straightforward Author Profiling Approach in MapReduce

  • Suraj Maharjan
  • Prasha Shrestha
  • Thamar Solorio
  • Ragib Hasan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8864)

Abstract

Most natural language processing tasks deal with large amounts of data, which takes a lot of time to process. For better results, a larger dataset and a good set of features are very helpful. But larger volumes of text and high dimensionality of features will mean slower performance. Thus, natural language processing and distributed computing are a good match. In the PAN 2013 competition, the test runtimes for author profiling range from several minutes to several days. Most author profiling systems available now are either inaccurate or slow or both. Our system, written entirely in MapReduce, employs nearly 3 million features and still manages to finish the task in a fraction of time than state-of-the-art systems and with better accuracy. Our system demonstrates that when we deal with a huge amount of data and/or a large number of features, using distributed systems makes perfect sense.

Keywords

Natural Language Processing Statistical Machine Translation Runtime Performance Hadoop Distribute File System Early Bird 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Rangel, F., Rosso, P., Koppel, M., Stamatatos, E., Inches, G.: Overview of the author profiling task at PAN: In: Notebook Papers of CLEF 2013 LABs and Workshops, CLEF-2013, Valencia, Spain, pp. 23–26 (September 2013)Google Scholar
  2. 2.
    Estival, D., Gaustad, T., Pham, S.B., Radford, W., Hutchinson, B.: Author profiling for english emails. In: Proceedings of the 10th Conference of the Pacific Association for Computational Linguistics, pp. 263–272 (2007)Google Scholar
  3. 3.
    Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Dziurzynski, L., Ramones, S.M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M.E.P., Ungar, L.H.: Personality, gender, and age in the language of social media: The open-vocabulary approach. PLoS ONE 8, e73791 (2013)CrossRefGoogle Scholar
  4. 4.
    Meina, M., Brodzinska, K., Celmer, B., Czoków, M., Patera, M., Pezacki, J., Wilk, M.: Ensemble-based classification for author profiling using various features. In: Notebook Papers of CLEF 2013 LABs and Workshops, CLEF-2013, Valencia, Spain (September 2013)Google Scholar
  5. 5.
    Santosh, K., Bansal, R., Shekhar, M., Varma, V.: Author profiling: Predicting age and gender from blogs. In: Notebook Papers of CLEF 2013 LABs and Workshops, CLEF-2013, Valencia, Spain (September 2013)Google Scholar
  6. 6.
    López-Monroy, A.P., Montes-y Gómez, M., Escalante, H.J., Villaseñor-Pineda, L., Villatoro-Tello, E.: INAOE’s participation at PAN’13 : Author profiling task. In: Notebook Papers of CLEF 2013 LABs and Workshops, CLEF-2013, Valencia, Spain (September 2013)Google Scholar
  7. 7.
    Eidelman, V., Wu, K., Ture, F., Resnik, P., Lin, J.: Mr. MIRA: Open-source large-margin structured learning on MapReduce. ACL System Demonstrations (2013)Google Scholar
  8. 8.
    Owen, S., Anil, R., Dunning, T., Friedman, E.: Mahout in action. Manning (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Suraj Maharjan
    • 1
  • Prasha Shrestha
    • 1
  • Thamar Solorio
    • 1
  • Ragib Hasan
    • 1
  1. 1.University of Alabama at BirminghamBirminghamAlabama

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