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A synchronous context free grammar for time normalization.

Steven Bethard1

  • 1University of Alabama at Birmingham, Birmingham, Alabama, USA, bethard@cis.uab.edu.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
|November 7, 2017
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Summary
This summary is machine-generated.

This study introduces a novel time normalization method using synchronous context-free grammar. The approach accurately converts natural language time expressions into standardized formats, outperforming existing systems.

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Time normalization is crucial for temporal information extraction.
  • Existing systems face challenges in accurately interpreting diverse time expressions.
  • Accurate temporal reasoning requires robust time normalization techniques.

Purpose of the Study:

  • To develop an advanced time normalization approach.
  • To improve the accuracy and robustness of converting natural language time expressions to normalized forms.
  • To establish a new benchmark in temporal information processing.

Main Methods:

  • Utilizing a synchronous context-free grammar for time normalization.
  • Mapping source language expressions to formal time manipulation operators (e.g., FindEnclosed, StartAtEndOf).
  • Employing an extended CYK+ algorithm for parsing and recursive operator application.

Main Results:

  • Developed a set of synchronous rules for English time expressions.
  • Achieved superior performance compared to HeidelTime, the top system in TempEval 2013.
  • Demonstrated effectiveness across four distinct time normalization corpora.

Conclusions:

  • The proposed synchronous context-free grammar approach offers a significant advancement in time normalization.
  • This method provides a more accurate and reliable way to process temporal expressions.
  • The system sets a new standard for temporal information extraction tasks.