Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
The ATIS spoken language systems pilot corpus
HLT '90 Proceedings of the workshop on Speech and Natural Language
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
From trees to predicate-argument structures
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
Strategies for lifelong knowledge extraction from the web
Proceedings of the 4th international conference on Knowledge capture
Automatic fine-grained semantic classification for domain adaptation
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
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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.