Building natural language generation systems
Building natural language generation systems
The Evaluation of a Personalised Health Information System for Patients with Cancer
User Modeling and User-Adapted Interaction
Graph-based generation of referring expressions
Computational Linguistics
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
Generation that exploits corpus-based statistical knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Suregen-2: a shell system for the generation of clinical documents
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Using a randomised controlled clinical trial to evaluate an NLG system
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Empirically-based control of natural language generation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Has a consensus NL generation architecture appeared, and is it psycholinguistically plausible?
INLG '94 Proceedings of the Seventh International Workshop on Natural Language Generation
Instance-based natural language generation
Natural Language Engineering
Controlling user perceptions of linguistic style: Trainable generation of personality traits
Computational Linguistics
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Images and videos resulting from diagnostic imaging procedures such as echocardiography need to be analyzed and interpreted by physicians in order to diagnose diseases of the patient. This process can be split into two steps: in a first step, various morphological features depicted in the images have to be interpreted and described. Then, a diagnostic conclusion has to be drawn from these observations. The first step can be facilitated by offering a structured entry form and some means to generate textual descriptions from the data entered in this form. While it is straight-forward to implement some basic text generation functionality using hard-wired text templates, the generation of fluent, well-readable text from structured data is much harder. In this collaboration we have combined advanced methods from computational linguistics and medical knowledge resources to solve this problem. We have built a prototype for the domain of echocardiography and evaluated it in a clinical setting.