Multi-View Active Learning for Short Text Classification in User-Generated Data
This work addresses the challenge of labeling short, informal texts for applications like disaster detection, offering an incremental improvement in active learning methods for this domain.
The paper tackles the problem of classifying short texts in user-generated data, such as Twitter posts, by proposing a multi-view active learning model that integrates representativeness measures and query-by-committee strategies, and it outperforms existing models on four publicly available datasets.
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.