Proceedings Abstracts of the Twenty-Fourth International Joint Conference on Artificial Intelligence

Interactive Gender Inference with Integer Linear Programming / 2341
Shoushan Li, Jingjing Wang, Guodong Zhou, Hanxiao Shi

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.