Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Web-scale information extraction in knowitall: (preliminary results)
Proceedings of the 13th international conference on World Wide Web
Near-duplicate detection for eRulemaking
dg.o '05 Proceedings of the 2005 national conference on Digital government research
A bootstrapping method for learning semantic lexicons using extraction pattern contexts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Multidimensional text analysis for eRulemaking
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Next steps in near-duplicate detection for eRulemaking
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Near-duplicate detection by instance-level constrained clustering
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying sources of opinions with conditional random fields and extraction patterns
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Learning domain-specific information extraction patterns from the Web
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
Ontology generation for large email collections
dg.o '08 Proceedings of the 2008 international conference on Digital government research
A cross-media method of stakeholder extraction for news contents analysis
WAIM'10 Proceedings of the 11th international conference on Web-age information management
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A stakeholder is an individual, group, organization, or community that has an interest or stake in a consensus-building process. The goal of stakeholder identification is identifying stakeholder mentions in natural language text. We present novel work in using a bootstrapping approach for the identification of stakeholders in public comment corpora. We refine the definition of a stakeholder by categorizing stakeholders into 2 distinct stakeholder types and experiment with automatically identifying one of these two types: instances where the author identifies him/herself as a member of a particular group. An existing bootstrapping information extraction algorithm is combined individually with 3 distinct extraction pattern templates. Results show that this stakeholder group can be learned in a minimally supervised bootstrapping framework using 2 of the 3 extraction pattern templates. An experimental analysis explores the challenges in applying the third extraction pattern template to this problem. Results on all 3 extraction pattern templates provide insight on the unique and novel challenge of identifying stakeholders.