Multi-Domain Active Learning for Text Classification

Active learning has been proven to be effective in reducing labeling efforts for supervised learning. However, existing active learning work has mainly focused on training models for a single domain. In practical applications, it is common to \emph{simultaneously} train classifiers for multiple domains. For example, some merchant web sites (like Amazon) may need a set of classifiers to predict the sentiment polarity of reviews which are collected from various domains (e.g., electronics, books, shoes). Though different domains have their own unique features, they may share some common latent features. If we apply active learning on each domain separately, some data instances selected from different domains may contain duplicate knowledge due to the common features. Therefore, how to \emph{jointly} select data from multiple domains to label is crucial to further reduce labeling efforts for multi-domain learning. In this paper, we propose a novel \emph{multi-domain active learning} framework based on Support Vector Machines to jointly select instances from all domains with duplicate information considered. In our solution, a shared subspace is first learned to represent common latent features of different domains. By considering the common and the domain-specific features together, the model loss reduction induced by each data candidate can be decomposed into a common part and a domain-specific part. In this way, the duplicate information across domains can be encoded into the common part of model loss reduction and taken into account when querying. We compare our method with the state-of-the-art active learning baselines on three text classification tasks: sentiment classification, newsgroup classification and email spam filtering. The experimental results show that our method reduce the human labeling efforts by 40.0\%, 50.0\% and 57.8\% respectively on three tasks compared with the state-of-the-art baselines.

In  The 18th ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2012)

Publisher  ACM

Details

TypeInproceedings
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