Interactive Gender Inference with Integer Linear Programming

نویسندگان

  • Shoushan Li
  • Jingjing Wang
  • Guodong Zhou
  • Hanxiao Shi
چکیده

Interactive gender inference aims to infer the genders of the two involved users in a communication from the interactive text. In this paper, we address this task by proposing a joint inference approach which well incorporates label correlations among the instances. Specifically, an Integer Linear Programming (ILP) approach is proposed to achieve global optimization with various kinds of intra-task and extra-task constraints. Empirical studies demonstrate the effectiveness of the proposed ILP-based approach to interactive gender inference. 1

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تاریخ انتشار 2015