Concept Lattice-Based Classification in NLP

Abstract

Classification in discrete object space is a widely used machine learning technique. In this case, we can construct a rule set using attribute level implication rules. In this paper, we apply the technique of formal concept analysis to generate the rule base of the classification. This approach is suitable for cases where the number of possible attribute subsets is limited. For testing of this approach, we investigated the problem of the part of speech prediction in natural language texts. The proposed model provides a better accuracy and execution cost than the baseline back-propagation neural network method.