Semantic Interpretation of Superlative Expressions via Structured Knowledge Bases

Abstract

This paper addresses a novel task of semantically analyzing the comparative constructions inherent in attributive superlative expressions against structured knowledge bases (KBs). The task can be defined in two-fold: first, selecting the comparison dimension against a KB, on which the involved items are compared; and second, determining the ranking order, in which the items are ranked (ascending or descending). We exploit Wikipedia and Freebase to collect training data in an unsupervised manner, where a neural network model is then learnt to select, from Freebase predicates, the most appropriate comparison dimension for a given superlative expression, and further determine its ranking order heuristically. Experimental results show that it is possible to learn from coarsely obtained training data to semantically characterize the comparative constructions involved in attributive superlative expressions.

A Survey of Knowledge Based Question Answering with Deep Learning

“…Zhang et al [7] tried to use attention mechanism in KBQA. The starting point is that for different types of answers, the focus of our concerns is different.…”