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Conditional random fields for entity extraction and ontological text coding

Author

Listed:
  • Jana Diesner

    (Carnegie Mellon University)

  • Kathleen M. Carley

    (Carnegie Mellon University)

Abstract

Previous research suggests that one field with a strong yet unsatisfied need for automatically extracting instances of various entity classes from texts is the analysis of socio-technical systems (Feldstein in Media in Transition MiT5, 2007; Hampe et al. in Netzwerkanalyse und Netzwerktheorie, 2007; Weil et al. in Proceedings of the 2006 Command and Control Research and Technology Symposium, 2006; Diesner and Carley in XXV Sunbelt Social Network Conference, 2005). Traditional as well as non-traditional and customized sets of entity classes and the relationships between them are often specified in ontologies or taxonomies. We present a Conditional Random Fields (CRF)-based approach to distilling a set of entities that are defined in an ontology originating from organization science. CRF, a supervised sequential machine learning technique, facilitates the derivation of relational data from corpora by locating and classifying instances of various entity classes. The classified entities can be used as nodes for the construction of socio-technical networks. We find the outcome sufficiently accurate (82.7 percent accuracy of locating and classifying entities) for future application in the described problem domain. We propose using the presented methodology as a crucial step in the process of advanced modeling and analysis of complex and dynamic networks.

Suggested Citation

  • Jana Diesner & Kathleen M. Carley, 2008. "Conditional random fields for entity extraction and ontological text coding," Computational and Mathematical Organization Theory, Springer, vol. 14(3), pages 248-262, September.
    • Handle: RePEc:spr:comaot:v:14:y:2008:i:3:d:10.1007_s10588-008-9029-z
    DOI: 10.1007/s10588-008-9029-z
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    Citations

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    Cited by:

    1. Kathleen M. Carley & Michael W. Bigrigg & Boubacar Diallo, 2012. "Data-to-model: a mixed initiative approach for rapid ethnographic assessment," Computational and Mathematical Organization Theory, Springer, vol. 18(3), pages 300-327, September.
    2. Christopher E. Hutchins & Marge Benham-Hutchins, 2010. "Hiding in plain sight: criminal network analysis," Computational and Mathematical Organization Theory, Springer, vol. 16(1), pages 89-111, March.
    3. Jürgen Pfeffer & Kathleen M. Carley, 2012. "Rapid modeling and analyzing networks extracted from pre-structured news articles," Computational and Mathematical Organization Theory, Springer, vol. 18(3), pages 280-299, September.
    4. Jana Diesner & Kathleen M. Carley & Laurent Tambayong, 2012. "Extracting socio-cultural networks of the Sudan from open-source, large-scale text data," Computational and Mathematical Organization Theory, Springer, vol. 18(3), pages 328-339, September.

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