Probing the Space of Optimal Markov Logic Networks for Sequence Labeling

Abstract

Discovering relational structure between input features in sequence labeling models has shown to improve their accuracies in several problem settings. The problem of learning relational structure for sequence labeling can be posed as learning Markov Logic Networks (MLN) for sequence labeling, which we abbreviate as Markov Logic Chains (MLC). This objective in propositional space can be solved efficiently and optimally by a Hierarchical Kernels based approach, referred to as StructRELHKL, which we recently proposed. However, the applicability of StructRELHKL in complex first order settings is non-trivial and challenging. We present the challenges and possibilities for optimally and simultaneously learning the structure as well as parameters of MLCs (as against learning them separately and/or greedily). Here, we look into leveraging the StructRELHKL approach for optimizing the MLC learning steps to the extent possible. To this end, we categorize first order MLC features based on their complexity and show that complex features can be constructed from simpler ones. We define a self-contained class of features called absolute features ($\mathcal{AF}$), which can be conjoined to yield complex MLC features. Our approach first generates a set of relevant $\mathcal{AF}$s and then makes use of the algorithm for StructRELHKL to learn their optimal conjunctions. We demonstrate the efficiency of our approach by evaluating on a publicly available activity recognition dataset.