Maria Liakata
ABSTRACT
In this paper we describe a method of automatically learning domain theories from parsed corpora of sentences from the relevant domain and use FSA techniques for the graphical representation of such a theory. By a 'domain theory' we mean a collection of facts and generalisations or rules which capture what commonly happens (or does not happen) in some domain of interest. As language users, we implicitly draw on such theories in various disambiguation tasks, such as anaphora resolution and prepositional phrase attachment, and formal encodings of domain theories can be used for this purpose in natural language processing. They may also be objects of interest in their own right, that is, as the output of a knowledge discovery process. The approach is generizable to different domains provided it is possible to get logical forms for the text in the domain.
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- Learning theories from text
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