Uncovering and Managing the Impact of Methodological Choices for the Computational Construction of Socio-Technical Networks from Texts

Abstract

This thesis is motivated by the need for scalable, robust and reliable methods and technologies that support the construction of network data from natural language text data, and the usage of the extracted data for answering substantive and graph-theoretical questions about sociotechnical networks. The findings and technology resulting from this thesis improve the applicability of language technologies for generating network data based on text data; thereby advancing the intersection of network analysis and text analysis. This thesis contributes to the actionable meaning of network data by providing methods that leverage theories from the social sciences to construct and analyze network data, and to combine text data and network data for analysis.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2012
Accession Number
AD1153027

Entities

People

  • Jana Diesner

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Automata Theory
  • Cognitive Science
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Data Mining
  • Geography
  • Information Processing
  • Information Science
  • Information Systems
  • Named Entity Recognition
  • Natural Language Processing
  • Network Science
  • Ontologies
  • Self Organizing Systems
  • Social Networking Services

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Organizational Process Management (OPM).