Paper Info
Title
Learning theories from text
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.
Year
DOI
Venue
2004
10.3115/1220355.1220382
COLING
Keywords
Field
DocType
graphical representation,fsa technique,domain theory,relevant domain,knowledge discovery process,natural language processing,language user,formal encodings,anaphora resolution,different domain,learning theory,logical form
Problem domain,Computer science,Learning theory,Phrase,Domain theory,Natural language processing,Knowledge extraction,Artificial intelligence,Parsing
Conference
Volume
Citations 
PageRank 
C04-1
5
0.59
References 
Authors
6
2
Name
Order
Citations
PageRank
Maria Liakata137530.40
Stephen Pulman245038.31