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Evaluating Supervised Semantic Parsing Methods on Application-Independent Data

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 8607)

Abstract

While supervised statistical semantic parsing methods have received a good amount of attention in recent years, this research has largely been done on small and specialized data sets. This paper introduces a work-in-progress with the objective of examining the applicability of supervised statistical semantic parsing to application-independent data with linguistically motivated meaning representations. The approach discussed in this paper has three key aspects: The circumvention of data scarcity using automatic annotation, experimentation with different types of meaning representations, and the design of a suitable graded evaluation measure.

Keywords

  • Natural Language Processing
  • Automatic Annotation
  • Computational Linguistics
  • Syntax Tree
  • Meaning Representation

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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Beschke, S. (2014). Evaluating Supervised Semantic Parsing Methods on Application-Independent Data. In: Colinet, M., Katrenko, S., Rendsvig, R.K. (eds) Pristine Perspectives on Logic, Language, and Computation. ESSLLI ESSLLI 2013 2012. Lecture Notes in Computer Science, vol 8607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44116-9_2

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  • DOI: https://doi.org/10.1007/978-3-662-44116-9_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44115-2

  • Online ISBN: 978-3-662-44116-9

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