Two-Way Parsimonious Classification Models for Evolving Hierarchies

Abstract

There is an increasing volume of semantically annotated data available, in particular due to the emerging use of knowledge bases to annotate or classify dynamic data on the web. This is challenging as these knowledge bases have a dynamic hierarchical or graph structure demanding robustness against changes in the data structure over time. In general, this requires us to develop appropriate models for the hierarchical classes that capture all, and only, the essential solid features of the classes which remain valid even as the structure changes. We propose hierarchical significant words language models of textual objects in the intermediate levels of hierarchies as robust models for hierarchical classification by taking the hierarchical relations into consideration. We conduct extensive experiments on richly annotated parliamentary proceedings linking every speech to the respective speaker, their political party, and their role in the parliament. Our main findings are the following. First, we define hierarchical significant words language models as an iterative estimation process across the hierarchy, resulting in tiny models capturing only well grounded text features at each level. Second, we apply the resulting models to party membership and party position classification across time periods, where the structure of the parliament changes, and see the models dramatically better transfer across time periods, relative to the baselines.