The “GENERATION GAP”: the problem of expressibility in text planning
The “GENERATION GAP”: the problem of expressibility in text planning
The acquisition of stress: a data-oriented approach
Computational Linguistics - Special issue on computational phonology
WordNet: a lexical database for English
Communications of the ACM
Building natural language generation systems
Building natural language generation systems
Conceptual Information Processing
Conceptual Information Processing
Lexical Options in Multilingual Generation from a Knowledge Base
EWNLG '93 Selected papers from the Fourth European Workshop on Trends in Natural Language Generation, An Artificial Intelligence Perspective
Case Retrieval Nets: Basic Ideas and Extensions
KI '96 Proceedings of the 20th Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Story plot generation based on CBR
Knowledge-Based Systems
Case retrieval nets for heuristic lexicalization in natural language generation
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Dependency Analysis and CBR to Bridge the Generation Gap in Template-Based NLG
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
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The present paper describes a case-based reasoning solution for solving the task of selecting adequate templates for realizing messages describing actions in a given domain. This solution involves the construction of a case base from a corpus of example texts, using information from WordNet to group related verbs together. A case retrieval net is used as a memory model. A taxonomy of the concepts involved in the texts is used to compute similarity between concepts. The set of data to be converted into text acts as a query to the system. The process of solving a given query may involve several retrieval processes – to obtain a set of cases that together constitute a good solution for transcribing the data in the query as text messages – and a process of knowledge-intensive adaptation which resorts to a knowledge base to identify appropriate substitutions and completions for the concepts that appear in the cases, using the query as a source. We describe this case-based solution, and we present examples of how it solves the task of selecting an appropriate set of templates to render a given set of data as text.