An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Information Extraction: Distilling Structured Data from Unstructured Text
Queue - Social Computing
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Hiding in plain sight: criminal network analysis
Computational & Mathematical Organization Theory
Analyzing scientific networks for nuclear capabilities assessment
Journal of the American Society for Information Science and Technology
Rapid modeling and analyzing networks extracted from pre-structured news articles
Computational & Mathematical Organization Theory
Data-to-model: a mixed initiative approach for rapid ethnographic assessment
Computational & Mathematical Organization Theory
Extracting socio-cultural networks of the Sudan from open-source, large-scale text data
Computational & Mathematical Organization Theory
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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.