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	Multi-task Learning for Author Profiling with Hierarchical Features
	Zhile Jiang; Shuai Yu; Min Yang; Qiang Qu; Junyu Luo; Juncheng Liu
	2018
会议日期	2018
会议地点	Lyon, France
英文摘要	Author profiling is an important but challenging task. In this paper, we propose a novel Multi-Task learning framework for Author Profiling (MTAP), in which a document modeling module is shared across three different author profiling tasks (i.e., age, gender and job classification tasks). To further boost author profiling, we integrate hierarchical features learned by different models. Concretely, we employ CNN, LSTM and topic model to learn the character-level, word-level and topic-level features, respectively. MTAP thus leverages the benefits of supervised deep neural neural networks as well as an unsupervised probabilistic generative model to enhance the document representation learning. Experimental results on a real-life blog dataset show that MTAP has robust superiority over competitors and sets state-of-the-art for all the three author profiling tasks
语种	英语
URL标识	查看原文
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/14087]
专题	深圳先进技术研究院_数字所
推荐引用方式 GB/T 7714	Zhile Jiang,Shuai Yu,Min Yang,et al. Multi-task Learning for Author Profiling with Hierarchical Features[C]. 见:. Lyon, France. 2018.

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