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Related Experiment Videos

How to design a connectionist holistic parser.

E K Ho1, L W Chan

  • 1Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.

Neural Computation
|December 1, 1999
PubMed
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Connectionist holistic parsing learns grammar from examples, offering an alternative to traditional methods. Exploring design choices reveals key factors for effective holistic parser development and robust language parsing.

Area of Science:

  • Computational Linguistics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional algorithmic parsers face limitations in learning grammatical structures.
  • Connectionist holistic parsers offer an inductive learning approach to parsing.
  • Previous connectionist parser designs have varied in their methodologies.

Purpose of the Study:

  • To explore various design dimensions of connectionist holistic parsers.
  • To compare the performance of different holistic parser designs.
  • To identify essential factors for building effective holistic parsers.

Main Methods:

  • Investigating sentence and parse tree encoding methods.
  • Examining the use of shared representations and confluent inference.

Related Experiment Videos

  • Analyzing the impact of including phrases in training data.
  • Comparing parsers like confluent preorder, backpropagation, XERIC, and modular connectionist models.

    • Main Results:

    • Different combinations of design factors yield distinct holistic parsers.
    • Experimental evaluation assesses generalization capability and robustness.
    • Performance comparison highlights the strengths and weaknesses of various models.

    Conclusions:

    • Understanding design dimensions is crucial for effective holistic parser construction.
    • Robustness and generalization are key performance indicators.
    • Further research can build upon identified essential issues for improved parsing.