Case-Based Reasoning for Knowledge-Intensive Template Selection During Text Generation

  • Raquel Hervás
  • Pablo Gervás
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)


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.


Knowledge Base Retrieval Process Fairy Tale Natural Language Generation Information Entity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Raquel Hervás
    • 1
  • Pablo Gervás
    • 1
  1. 1.Departamento de Sistemas Informáticos y ProgramaciónUniversidad Complutense de MadridSpain

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