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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
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)
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)
Eidelman, V., Wu, K., Ture, F., Resnik, P., Lin, J.: Mr. MIRA: Open-source large-margin structured learning on MapReduce. ACL System Demonstrations (2013)
Owen, S., Anil, R., Dunning, T., Friedman, E.: Mahout in action. Manning (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Maharjan, S., Shrestha, P., Solorio, T., Hasan, R. (2014). A Straightforward Author Profiling Approach in MapReduce. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-12027-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12026-3
Online ISBN: 978-3-319-12027-0
eBook Packages: Computer ScienceComputer Science (R0)