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Augmenting formal semantic representation for NLP: The story of SMEARR

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Abstract

The centerpiece of the paper is SMEARR, an enriched and augmented lexical database with a database management system and several peripherals. It is presented as a polytheoretical shareable resource in computational semantics and justified as a manageable empirically-based study of the meaning bottleneck in NLP. The relation between SMEARR entries and the examples of formal semantic descriptions in the generative and post-generative semantic literature is explored, revealing a significant discrepancy in the formalisms and in the very nature of formality between computational and theoretical semantics. Finally, the idea of variable-depth semantics, developed in earlier publications, is brought up in the context of SMEARR.

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Authors and Affiliations

  1. NLP Lab, Purdue University, 47907, W. Lafayette, IN, USA

    Victor Raskin

  2. Department of English, Youngstown, State University, 44555, Youngstown, OH, USA

    Salvatore Attardo

  3. Center for Machine Translation, Carnegie Mellon University, 15213, Pittsburgh, PA, USA

    Donalee H. Attardo

Authors
  1. Victor Raskin
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  2. Salvatore Attardo
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  3. Donalee H. Attardo
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Raskin, V., Attardo, S. & Attardo, D.H. Augmenting formal semantic representation for NLP: The story of SMEARR. Mach Translat 9, 81–98 (1994). https://doi.org/10.1007/BF00986323

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