Aggregation in Natural Language Generation
EWNLG '93 Selected papers from the Fourth European Workshop on Trends in Natural Language Generation, An Artificial Intelligence Perspective
Segregatory coordination and ellipsis in text generation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Generation that exploits corpus-based statistical knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Exploiting a probabilistic hierarchical model for generation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Extraposition: a case study in German sentence realization
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning to predict case markers in Japanese
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Stochastic realisation ranking for a free word order language
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
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We show that it is possible to learn the contexts for linguistic operations which map a semantic representation to a surface syntactic tree in sentence realization with high accuracy. We cast the problem of learning the contexts for the linguistic operations as classification tasks, and apply straightforward machine learning techniques, such as decision tree learning. The training data consist of linguistic features extracted from syntactic and semantic representations produced by a linguistic analysis system. The target features are extracted from links to surface syntax trees. Our evidence consists of four examples from the German sentence realization system code-named Amalgam: case assignment, assignment of verb position features, extraposition, and syntactic aggregation.