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Sharpening semantic gradient in a planarized sentence representation.

Caiwei Yang1, Yanping Chen1, Shuai Yu1

  • 1Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, PR China; State Key Laboratory of Public Big Data, Guizhou university, Guiyang, 550025, Guizhou, PR China.

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|June 17, 2025
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Summary
This summary is machine-generated.

This study introduces a method to sharpen semantic gradients in 2D sentence representations, improving information extraction. By clarifying linguistic unit boundaries, this approach enhances the understanding of sentence structures.

Keywords:
Information extractionPlanarized sentence representationSemantic gradient

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Two-dimensional sentence representations unfold semantic structures and token interactions.
  • Overlapping linguistic units in planarized representations can blur semantic boundaries.
  • Semantic information penetration between neighboring elements poses a challenge for accurate representation.

Purpose of the Study:

  • To reveal the mechanism of sharpening the semantic gradient in planarized sentence representations.
  • To improve the accuracy of sentence-level information extraction by addressing blurred semantic edges.

Main Methods:

  • Developing a method to sharpen the semantic gradient in 2D sentence representations.
  • Evaluating the proposed method on six diverse datasets.
  • Analyzing the impact of semantic gradient sharpening on linguistic unit representation.

Main Results:

  • The method demonstrates impressive improvements on three distinct information extraction tasks.
  • Sharpening the semantic gradient effectively mitigates noise from neighboring elements.
  • The approach successfully enhances the clarity of semantic edges in planarized sentences.

Conclusions:

  • Representing and processing sentences in 2D has significant potential for decoding semantic structures.
  • The proposed semantic gradient sharpening technique offers a promising direction for sentence-level information extraction.
  • The findings highlight the importance of precise semantic boundary definition in natural language processing.