Semantic-head-driven generation
Computational Linguistics
The “GENERATION GAP”: the problem of expressibility in text planning
The “GENERATION GAP”: the problem of expressibility in text planning
Handbook of formal languages, vol. 3
Planning English Sentences
Characterizing mildly context-sensitive grammar formalisms
Characterizing mildly context-sensitive grammar formalisms
Mathematical and computational aspects of lexicalized grammars
Mathematical and computational aspects of lexicalized grammars
The problem of logical-form equivalence
Computational Linguistics - Special issue on using large corpora: I
Sentence planning as description using tree adjoining grammar
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
An efficient kernel for multilingual generation in speech-to-speech dialogue translation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
XTAG system: a wide coverage grammar for English
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Towards a Natural Language Driven Automated Help Desk
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
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This paper describes an integrated generation system (INLGS) based on the formalism of Schema Tree Adjoining Grammars with Unification (SU-TAGs). According to this system architecture, all knowledge bases are specified in the same formalism and run the same processing algorithm. A main advantage is that negotiation between generation components can easily be imposed on the system. Moreover, only one algorithm must be implemented and tested in order to provide the one and only processing unit. In the INLGS a reversible parser/generator is deployed. It runs knowledge bases in the formalism of SU-TAGs. SU-TAG comprises a condensed grammar representation and direct parsing/ generation deals with partially unspecified schemata. Instead of developing new knowledge bases from scratch, existing ones are reused here. This means all knowledge bases of the INLGS are transformed (e.g., the TAG-based XTAG system and the plan-based interpersonal model of VOTE).