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An Analogical Learner For Morphological Analysis: Proceedings Of CoNLL 2005.pdf

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Preview of An Analogical Learner for Morphological Analysis: Proceedings of CoNLL 2005
🔗 Source: aclanthology.org
📊 Size: 153 KB
👤 Author: Association for Computational Linguistics
⬇️ Downloads: 200

Summary

The approach leverages surface analogies between linguistic representations to learn deeper semantic similarities, addressing the challenge of high dimensionality in NLP databases.

Introduction:

Analogical learning, based on a two-step inductive process, constructs structural mappings between new and solved instances, then induces solutions from these mappings. This requires defining and efficiently computing analogical relationships. Previous attempts using surface analogies focused on linear representations and ad-hoc definitions.

Principles of Analogical Learning (Section 2):

The paper discusses different models of analogical reasoning and learning, highlighting the importance of identifying structural mappings between seemingly unrelated situations. It distinguishes between symbolic and subsymbolic approaches, emphasizing the need for a flexible model that can handle complex tasks like morphological analysis.

Algebraic Framework and Instantiations (Section 3):

Presents a general algebraic framework for formal analogical proportions and its application to words and labeled trees. It discusses the algorithmic complexity of the inference procedure, demonstrating the model's flexibility and accuracy through experimental results on morphological analysis tasks.

Experiments and Results (Section 4):

The paper reports experiments validating the model's effectiveness in learning morphological regularities. It concludes by discussing current limitations and suggesting potential extensions.

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Description

Presented by Stroppa and Yvon in 2005 at CoNLL, it explores a two-step inference process for structural mapping and knowledge transfer.

Technical Information

  • File Format: PDF
  • File Size: 153 KB
  • Pages: 8
  • Language: EN
  • Author: Association for Computational Linguistics
  • Total Downloads: 200
  • Last Updated: 2 weeks ago

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