Using ILP to learn a domain theory in the form of a FSA

This paper describes a method for inducing a domain theory from a corpus of parsed sentences by means of ILP techniques. A ‘domain theory’ in the current context stands for a collection of facts and generalisations or rules which describe entities and relations between entities within a domain of interest. As language users, we implicitly draw on such theories in various disambiguation tasks, such as anaphora resolution and prepositional phrase attachment, or to draw inferences. Formal encodings of domain theories can be used for this purpose in natural language processing but they may also be objects of interest in their own right, that is, as the output of a knowledge discovery process. The patterns learnt are represented as FSAs, providing a graphical representation and a more compact format than the original symbolic rules. The approach is generizable to different domains provided it is possible to get logical forms for the text in the domain.