Transition network grammars for natural language analysis
Communications of the ACM
The Theory and Practice of Augmented Transition Network Grammars
Natural Language Communication with Computers
Using focus for generating felicitous locative expressions
IEA/AIE '90 Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
ACM '86 Proceedings of 1986 ACM Fall joint computer conference
Tracking point of view in narrative
Computational Linguistics
Uniform knowledge representation for language processing in the B2 system
Natural Language Engineering
Quasi-indexical reference in propositional semantic networks
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
Meinongian semantics for propositional semantic networks
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Natural language with integrated deictic and graphic gestures
HLT '89 Proceedings of the workshop on Speech and Natural Language
How Helen Keller used syntactic semantics to escape from a Chinese Room
Minds and Machines
A representation for natural category systems
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
The augmented transition network (ATN) is a formalism for writing parsing grammars that has been much used in Artificial Intelligence and Computational Linguistics. A few researchers have also used ATNs for writing grammars for generating sentences. Previously, however, either generation ATNs did not have the same semantics as parsing ATNs, or they required an auxiliary mechanism to determine the syntactic structure of the sentence to be generated. This paper reports a generalization of the ATN formalism that allows ATN grammars to be written to parse labelled directed graphs. Specifically, an ATN grammar can be written to parse a semantic network and generate a surface string as its analysis. An example is given of a combined parsing-generating grammar that parses surface sentences, builds and queries a semantic network knowledge representation, and generates surface sentences in response.