ABSTRACT N-gram feature templates that consider consecutive contextual information comprise a family of important feature templates used in structured prediction. Some previous studies considered the n-gram feature selection problem but they focused on one or several types of features in certain tasks, e.g., consecutive words in a text categorization task. In this paper, we propose a fast and robust bottomup wrapper method for automatically inducing n-gram feature templates, which can induce any type of n-gram feature for any structured prediction task. According to the significance distribution for n-gram feature templates based on the n-gram and bias (offset), the proposed method first determines the n-gram that achieves the best tradeoff between the severity of the sparse data problem with n-gram feature templates and the richness of the corresponding contextual information, before combining the best n-gram with lower-order gram templates in an extremely efficient manner. In addition, our method uses a template pair, i.e., the two symmetrical templates, rather than a template as the basic unit (i.e., including or excluding a template pair rather than a template). Thus, when the data in the training set change slightly, our method is robust to this fluctuation, thereby providing a more consistent induction result compared with the templatebased method. The experimental results obtained for three tasks, i.e., Chinese word segmentation, named entity recognition, and text chunking, demonstrated the effectiveness, efficiency, and robustness of the proposed method. INDEX TERMS Feature induction, n-gram feature template, structured prediction.
For example, Ren and Li REF presented a wrapper method to deduce n-gram feature templates.
Fast and Robust Wrapper Method for $N$ -gram Feature Template Induction in Structured Prediction
IEEE Access
IEEE Access
Computer Science