Creating a Natural Logic Inference System with Combinatory Categorial Grammar
This dissertation presents an integrated system for producing Natural Logic inferences, which are used in a wide variety of natural language understanding tasks. Natural Logic is the process of creating valid inferences by making incremental edits to natural language expressions with respect to a universal monotonicity calculus, without resorting to logical representation of the expressions (using First Order Logic for instance) The system generates inferences from surface forms using a three stage process. First, each sentence is subjected to syntactic analysis, using a purpose built syntactic parser. Then the rules of the monotonicity calculus are applied, specifying the directionality of entailment for each sentence constituents. A constituent can be either upward or downward entailing, meaning that we may replace it with a semantically broader or narrower term. Finally, we can find all suitable replacement terms for each target word by using the WordNet lexical database, which contains hypernymic and hyponymic relations. Using Combinatory Categorial Grammar, we were able to incorporate the monotonicity determination step in the syntactic derivation process. In order to achieve wide coverage over English sentences we had to introduce statistical models into our syntactic parser. At the current stage we have implemented a simple statistical model similar to those of Probabilistic Context Free Grammars. The system is intended to provide input to "deep" reasoning engines, used for higher level Natural Language Processing applications such as Recognising Textual Entailment. In addition to independently evaluating each component of the system, we present our comprehensive findings using Cyc, a large scale knowledge base, and we outline a solution for its relatively limited concept coverage.