Automatic labeling of semantic roles
Computational Linguistics
Learning probabilistic lexicalized grammars for natural language processing
Learning probabilistic lexicalized grammars for natural language processing
Kernel methods for relation extraction
The Journal of Machine Learning Research
Text mining techniques for patent analysis
Information Processing and Management: an International Journal
Structuring technological information for technology roadmapping: data mining approach
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
TextRunner: open information extraction on the web
NAACL-Demonstrations '07 Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Semantic relations in information science
Annual Review of Information Science and Technology
Technology management simply defined: A tweet plus two characters
Journal of Engineering and Technology Management
Developing a Dataset for Technology Structure Mining
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
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This paper introduces the task of Technology-Structure Mining to support Management of Technology. We propose a linguistic based approach for identification of Technology Interdependence through extraction of technology concepts and relations between them. In addition, we introduce Technology Structure Graph for the task formalization. While the major challenge in technology structure mining is the lack of a benchmark dataset for evaluation and development purposes, we describes steps that we have taken towards providing such a benchmark. The proposed approach is initially evaluated and applied in the domain of Human Language Technology and primarily results are demonstrated. We further explain plans and research challenges for evaluation of the proposed task.