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Structuralism01:26

Structuralism

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Structuralism, an early psychological theory developed by Wilhelm Wundt and his student Edward Bradford Titchener, sought to dissect the human mind into its most fundamental components. Wundt's groundbreaking work in his laboratory set the stage for Titchener to define structuralism's goal as cataloging the "atoms" of the mind—sensations, images, and feelings—akin to how chemists identify elements of matter.
Titchener's approach to structuralism was unique. He...
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Methods of Documentation I: Source-Oriented Records01:18

Methods of Documentation I: Source-Oriented Records

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Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
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Introduction to Structures01:30

Introduction to Structures

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A structure is defined as a system of interconnected members designed to support or transfer forces and successfully withstand the loads acting on them. The internal forces of a structure can be determined by decomposing the structure and analyzing the free-body diagrams of the individual members or of a combination of members. This helps in understanding the structural elements' behavior and ensuring that the structure is stable and can withstand the subjected loads.
There are three main...
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Structural Isomerism02:34

Structural Isomerism

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Isomerism in Complexes
Isomers are different chemical species that have the same chemical formula. Structural isomerism of coordination compounds can be divided into two subcategories, the linkage isomers and coordination-sphere isomers.
Linkage isomers occur when the coordination compound contains a ligand that can bind to the transition metal center through two different atoms. For example, the CN− ligand can bind through the carbon atom or through the nitrogen atom. Similarly, SCN− can...
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Structural Properties and Dimensions of Lumber01:21

Structural Properties and Dimensions of Lumber

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Wood's structural properties derive from fibers aligned along the tree's length, contributing significantly to its mechanical strength. Wood exhibits up to twenty times greater tensile strength along these fibers compared to across them, and generally shows better performance under compression than tension. The length of fibers varies, with hardwoods having fibers around one twenty-fifth inch long and softwoods ranging from one-eighth to one-third inch.
The strength characteristics of...
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Structures of Carboxylic Acid Derivatives01:28

Structures of Carboxylic Acid Derivatives

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Structure of Carboxylic Acid Derivatives
Carboxylic acid derivatives contain an acyl group attached to a heteroatom such as chlorine, oxygen, or nitrogen. The carbonyl carbon and oxygen are both sp2-hybridized with an unhybridized p orbital.
The three sp2 orbitals of the carbonyl carbon form three σ bonds, one each with the carbonyl oxygen, the α carbon, and the heteroatom, whereas the other two sp2 orbitals of the carbonyl oxygen are occupied by the lone pairs. Further, the unhybridized p...
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Related Experiment Video

Updated: Jan 7, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Representation learning approach for understanding structured documents.

Akkshita Trivedi1, Sandeep Khanna2, Santanu Chaudhury3

  • 1Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India. trivedi.2@iitj.ac.in.

Scientific Reports
|December 26, 2025
PubMed
Summary
This summary is machine-generated.

Document Relationship Entity Embedding Learner (D-REEL) improves document understanding by modeling semantic relationships between text, figures, and tables. This novel framework enhances correlation accuracy and extraction performance across complex layouts.

Keywords:
Document layoutInformation extractionRepresentation learningSemantic correlation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Current document understanding methods struggle with complex layouts and logical connections between elements.
  • Existing approaches fail to capture deep semantic relationships across diverse document structures.

Purpose of the Study:

  • Introduce a novel representation learning framework, Document Relationship Entity Embedding Learner (D-REEL), to model intricate semantic relationships within documents.
  • Enhance document understanding by accurately assessing semantic correlations between document fields, irrespective of their spatial arrangement.

Main Methods:

  • D-REEL generates extraction candidates and learns dense vector representations (embeddings) for each.
  • Compares embeddings to assess semantic correlations and determine article relationships.
  • Combines spatial information with domain-specific schemas for precise extraction and correlation scoring.
  • Proposes Semantic Structural Congruence (SSC) metric using location-agnostic localization.

Main Results:

  • Significant improvements in correlation accuracy and extraction performance demonstrated on public datasets.
  • Achieved an average mAP increment of 2-3% for correlation accuracy.
  • Obtained an SSC improvement of almost 10% for the PRIMA dataset, indicating effective relationship measurement.

Conclusions:

  • D-REEL offers a robust solution for understanding complex document layouts and semantic relationships.
  • The framework enables precise extraction and reliable correlation scoring, even with irregular document structures.
  • The proposed SSC metric effectively quantifies semantic structural congruence in documents.