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A System for Converting and Recovering Texts Managed as Structured Information.

Edgardo Samuel Barraza Verdesoto1,2,3, Marlly Yaneth Rojas Ortiz4, Richard de Jesus Gil Herrera5

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This study presents a novel system for structuring unstructured text, inspired by brain memory models. This adaptable architecture efficiently processes diverse data types for applications in natural language generation and data mining.

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Area of Science:

  • Cognitive Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Unstructured text data (reports, emails, web content) poses challenges for traditional SQL databases.
  • Existing methods often struggle with efficient and accurate processing of diverse textual information.

Purpose of the Study:

  • To introduce a system for converting unstructured textual information into a structured format.
  • To develop an adaptable architecture based on scientific models of memory.
  • To demonstrate the system's applicability, with a focus on the Spanish language.

Main Methods:

  • Employed an incremental prototyping approach to design the system architecture.
  • Integrated strategies derived from scientific models of brain memory recording and recovery.
  • Tested a specific implementation for the Spanish language.

Main Results:

  • Developed a flexible architecture capable of language adaptation.
  • Successfully demonstrated the system's potential for handling unstructured text.
  • Validated the approach with a case study in Spanish.

Conclusions:

  • The proposed system offers a robust solution for managing and utilizing unstructured text data.
  • This conversion facilitates applications in Natural Language Generation, Data Mining, and dynamic theory generation.
  • The brain-inspired architecture provides a novel framework for information processing.