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

  • Raquel Hervás
  • Pablo Gervás
Conference paper
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 


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Copyright information

© 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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