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From: jair-ed@ptolemy.arc.nasa.gov
Subject: New Article, SCREEN: Learning a Flat ...
Message-ID: <1997Jan31.011550.14928@ptolemy-ethernet.arc.nasa.gov>
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Date: Fri, 31 Jan 1997 01:15:50 GMT
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JAIR is pleased to announce the publication of the following article:

Wermter, S. and Weber, V. (1997)
  "SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis 
Using Artificial Neural Networks", Volume 6, pages 35-85.

   Available in HTML, Postscript (1.1M) and compressed Postscript (290K).
   For quick access via your WWW browser, use this URL:
     http://www.cs.washington.edu/research/jair/abstracts/wermter97a.html
   More detailed instructions are below.

   Abstract: Previous approaches of analyzing spontaneously spoken
   language often have been based on encoding syntactic and semantic
   knowledge manually and symbolically. While there has been some
   progress using statistical or connectionist language models, many
   current spoken- language systems still use a relatively brittle,
   hand-coded symbolic grammar or symbolic semantic component.<p>
   
   In contrast, we describe a so-called screening approach for learning
   robust processing of spontaneously spoken language.  A screening
   approach is a flat analysis which uses shallow sequences of category
   representations for analyzing an utterance at various syntactic,
   semantic and dialog levels.  Rather than using a deeply structured
   symbolic analysis, we use a flat connectionist analysis.  This
   screening approach aims at supporting speech and language processing
   by using (1) data-driven learning and (2) robustness of connectionist
   networks.  In order to test this approach, we have developed the
   SCREEN system which is based on this new robust, learned and flat
   analysis.<p>
   
   In this paper, we focus on a detailed description of SCREEN's
   architecture, the flat syntactic and semantic analysis, the
   interaction with a speech recognizer, and a detailed evaluation
   analysis of the robustness under the influence of noisy or incomplete
   input.  The main result of this paper is that flat representations
   allow more robust processing of spontaneous spoken language than
   deeply structured representations.  In particular, we show how the
   fault-tolerance and learning capability of connectionist networks can
   support a flat analysis for providing more robust spoken-language
   processing within an overall hybrid symbolic/connectionist framework.

The article is available via:
   
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    The compressed PostScript file is named wermter97a.ps.Z (290K)

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