Abstract
There are two extreme stances in mechanizing natural language inference. One seeks to reformulate a raw message so as to conform with the syntax and semantics of some formal logical system (such as FOL) suited for reliable, potentially deep general reasoning. The other uses what has become known as Natural Logic—an easy but shallow way of treating natural language itself as logic and reasoning directly on this level. Finding the right balance between these opposing stances is one of the key tasks in advancing the ability of machines to understand human language, and thus, for example, extract knowledge inferentially from text. In this paper, we provide arguments and evidence that EPILOG, a general reasoner for the natural language-like Episodic Logic, can be equipped with the knowledge needed for effective Natural Logic-like inference while also providing greater generality.
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