Mining user-generated data often suffers from the lack of enough labeled data, short document lengths, and the informal user language. In this paper, we propose a novel active learning model to overcome these obstacles in the tasks tailored for query phrases--e.g., detecting positive reports of natural disasters. Our model has three novelties: 1) It is the first approach to employ multi-view active learning in this domain. 2) It uses the Parzen-Rosenblatt window method to integrate the representativeness measure into multi-view active learning. 3) It employs a query-by-committee strategy, based on the agreement between predictors, to address the usually noisy language of the documents in this domain. We evaluate our model in four publicly available Twitter datasets with distinctly different applications. We also compare our model with a wide range of baselines including those with multiple classifiers. The experiments testify that our model is highly consistent and outperforms existing models.
翻译:采矿用户生成的数据往往缺乏足够的标签数据、文件长度短和非正式用户语言。在本文件中,我们提出一个新的积极学习模式,以克服在为查询短语(例如,探测自然灾害的正面报告)量身定制的任务中遇到的这些障碍。我们的模型有三个新颖之处:1)这是在这一领域采用多视角积极学习的第一个方法。2)它使用Parzen-Rosenblatt窗口方法,将代表性计量纳入多视角积极学习。3)它采用基于预测者之间协议的逐个查询战略,以解决这一领域文件通常十分吵闹的语言。我们用四个公开提供的具有不同应用的推特数据集来评估我们的模型。我们还将我们的模型与包括多分类器在内的各种基线进行比较。实验证明我们的模型高度一致,并超越了现有的模型。