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Research Article | Open Access |

Gender Prediction for Expert Finding Task

Author 1: Daler Ali Author 2: Malik Muhammad Saad Missen Author 3: Nadeem Akhtar Author 4: Nadeem Salamat Author 5: Hina Asmat Author 6: Amnah Firdous
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 7, No. 5 · Published 2016

DOI: 10.14569/IJACSA.2016.070525

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Abstract

Predicting gender by names is one of the most interesting problems in the domain of Information Retrieval and expert finding task. In this research paper, we propose a machine learning approach for gender prediction task. We propose a new feature, that is, combination of letters in names which gives 86.54% accuracy. Our data collection consists of 3000 Urdu language names written using English Alphabets. This technique can be used to extract names from email addresses and hence is also valid for emails. To the best of our knowledge, it is the first- ever attempt for predicting gender from Pakistani (Urdu) names written using English alphabets.

Keywords

Urdu Semantic Web Gender Prediction Expert Profiling Machine Learning

How to Cite this Article

Ali, D., Missen, M. M. S., Akhtar, N., Salamat, N., Asmat, H., & Firdous, A. (2016). Gender Prediction for Expert Finding Task. International Journal of Advanced Computer Science and Applications, 7(5). https://doi.org/10.14569/IJACSA.2016.070525

Ali, Daler, et al.. "Gender Prediction for Expert Finding Task." International Journal of Advanced Computer Science and Applications, vol. 7, no. 5, 2016, https://doi.org/10.14569/IJACSA.2016.070525.

@article{Ali2016,
  title     = {Gender Prediction for Expert Finding Task},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {5},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Daler Ali and Malik Muhammad Saad Missen and Nadeem Akhtar and Nadeem Salamat and Hina Asmat and Amnah Firdous},
  doi       = {10.14569/IJACSA.2016.070525},
  url       = {https://doi.org/10.14569/IJACSA.2016.070525}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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Volume 17 No. 11, November 2026
Paper Submission Due October 25, 2026
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