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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Deep Neural Networks for Image-Based Dietary Assessment
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Span-based model for overlapping entity recognition and multi-relations classification in the food domain.

Mengqi Zhang1,2, Lei Ma1,2, Yanzhao Ren3

  • 1School of E-business and Logistics, Beijing Technology and Business University, Beijing 100048, China.

Mathematical Biosciences and Engineering : MBE
|April 18, 2022
PubMed
Summary

This study introduces SpIE, a novel span-based model for information extraction (IE) from Chinese food recipes. SpIE improves accuracy by considering entity attributes, outperforming existing methods in recognizing overlapping entities and classifying multiple relations.

Keywords:
category markerentity attributesinformation extractionmulti-relations classificationoverlapping entity recognitionspan-based approach

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Information extraction (IE) is vital for knowledge graph construction, especially in the food domain for safety and ingredient analysis.
  • Chinese IE presents unique challenges due to complex sentence structures, rich word combinations, and lack of tense, particularly in knowledge-dense food contexts.
  • Existing IE models often overlook entity-specific features and attribute influences on inter-entity relationships, limiting performance in complex scenarios.

Purpose of the Study:

  • To address challenges in Chinese food domain IE, specifically overlapping entity recognition and multi-relation classification.
  • To propose a novel span-based information extraction (SpIE) model that incorporates entity attributes.
  • To enhance the accuracy and robustness of IE in specialized domains like food recipes.

Main Methods:

  • Developed SpIE, a span-based model for IE, transforming Named Entity Recognition (NER) into a classification task.
  • Utilized span representations to capture span-level features for each candidate entity.
  • Integrated entity attributes (mention and type) into the relation classification (RC) model to account for their influence on relationships.

Main Results:

  • SpIE significantly outperforms previous neural approaches on two benchmark datasets for Chinese food IE.
  • The model demonstrates superior performance in handling overlapping entity recognition and multi-relation classification tasks.
  • SpIE effectively captures crucial entity features and attribute influences, leading to improved IE accuracy.

Conclusions:

  • The proposed SpIE model offers a significant advancement in Chinese information extraction, particularly for complex domains like food.
  • Incorporating entity attributes is crucial for improving IE performance, especially in handling overlapping entities and multiple relations.
  • SpIE provides a competitive and effective solution for general IE tasks and specialized applications.