Markov Logic Network for Metaphor Set Expansion

Metaphor is a figure of speech, that allow us to understand a concept of a domain in terms of the other. One of the sub-problems related to the metaphor recognition is of metaphor set expansion. This in turn is an instance of information completion problem. We, in this work, propose an MLN based approach to address the problem of metaphor set expansion. The rules for metaphor set expansion are represented in the first order logic formulas. The rules are either soft or hard depending on the nature of the rules according to which corresponding logic formulas are then assigned weights. Many a times new metaphors are created based on usages of Is-A pair knowledge base. We, in this work model this phenomena by introducing appropriate predicates and formulas in clausal form. For experiments, we have used dataset from Microsoft concept graph consisting Is-A patterns. The experiments show that the weights for the formulas can be learnt using the training dataset. Moreover the formulas and their weights are easy to interpret and in-turn explains the inference results adequately. We believe that this is a first effort reported which uses MLN for metaphor set expansion.