Author name cluster

Yi-Fan Yan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2020 Conference Paper

Active Learning with Query Generation for Cost-Effective Text Classification

Yi-Fan Yan
Sheng-Jun Huang
Shaoyi Chen
Meng Liao
Jin Xu

Labeling a text document is usually time consuming because it requires the annotator to read the whole document and check its relevance with each possible class label. It thus becomes rather expensive to train an effective model for text classiﬁcation when it involves a large dataset of long documents. In this paper, we propose an active learning approach for text classiﬁcation with lower annotation cost. Instead of scanning all the examples in the unlabeled data pool to select the best one for query, the proposed method automatically generates the most informative examples based on the classiﬁcation model, and thus can be applied to tasks with large scale or even inﬁnite unlabeled data. Furthermore, we propose to approximate the generated example with a few summary words by sparse reconstruction, which allows the annotators to easily assign the class label by reading a few words rather than the long document. Experiments on different datasets demonstrate that the proposed approach can effectively improve the classiﬁcation performance while signiﬁcantly reduce the annotation cost.

PDF Details

IJCAI Conference 2018 Conference Paper

Cost-Effective Active Learning for Hierarchical Multi-Label Classification

Yi-Fan Yan
Sheng-Jun Huang

Active learning reduces the labeling cost by actively querying labels for the most valuable data. It is particularly important for multi-label learning, where the annotation cost is rather high because each instance may have multiple labels simultaneously. In many multi-label tasks, the labels are organized into hierarchies from coarse to fine. The labels at different levels of the hierarchy contribute differently to the model training, and also have diverse annotation costs. In this paper, we propose a multi-label active learning approach to exploit the label hierarchies for cost-effective queries. By incorporating the potential contribution of ancestor and descendant labels, a novel criterion is proposed to estimate the informativeness of each candidate query. Further, a subset selection method is introduced to perform active batch selection by balancing the informativeness and cost of each instance-label pair. Experimental results validate the effectiveness of both the proposed criterion and the selection method.

PDF Details