Abstract
In this paper, we define a novel task named interactive gender inference, which aims to utilize interactive text to identify the genders of two interactive users. To address this task, we propose a two stage approach by well incorporating the dependency among the interactive samples sharing identical users. Specifically, we first apply a standard four-category classification algorithm to get a preliminary result, and then propose a global optimization algorithm to achieve better performance. Evaluation demonstrates the effectiveness of our proposed approach to interactive gender inference.
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Acknowledgments
This research work has been partially supported by three NSFC grants, No. 61273320, No.61375073, No.61331011, and Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Zhu, Z., Wang, J., Li, S., Zhou, G. (2015). Interactive Gender Inference in Social Media. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9052. Springer, Cham. https://doi.org/10.1007/978-3-319-22324-7_24
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DOI: https://doi.org/10.1007/978-3-319-22324-7_24
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