Improving discriminative sequential learning by discovering important association of statistics

Abstract

Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing or information extraction. Their key advantage is the ability to capture various nonindependent and overlapping features of inputs. However, several unexpected pitfalls have a negative influence on the model's performance; these mainly come from a high imbalance among classes, irregular phenomena, and potential ambiguity in the training data. This article presents a data-driven approach that can deal with such difficult data instances by discovering and emphasizing important conjunctions or associations of statistics hidden in the training data. Discovered associations are then incorporated into these models to deal with difficult data instances. Experimental results of phrase-chunking and named entity recognition using CRFs show a significant improvement in accuracy. In addition to the technical perspective, our approach also highlights a potential connection between association mining and statistical learning by offering an alternative strategy to enhance learning performance with interesting and useful patterns discovered from large datasets.