Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Language-Independent Set Expansion of Named Entities Using the Web
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Coupling semi-supervised learning of categories and relations
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Empirical studies in learning to read
FAM-LbR '10 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading
Filling knowledge gaps in text for machine reading
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Coreference for learning to extract relations: yes, Virginia, coreference matters
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Ontology extraction and integration from semi-structured data
AMT'11 Proceedings of the 7th international conference on Active media technology
Connecting Two (or Less) Dots: Discovering Structure in News Articles
ACM Transactions on Knowledge Discovery from Data (TKDD)
Extreme extraction: machine reading in a week
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Collective intelligence as a source for machine learning self-supervision
Proceedings of the 4th International Workshop on Web Intelligence & Communities
Green-Thumb camera: LOD application for field IT
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
A field application of LOD: LOD extraction from web and LOD search by sensor
Proceedings of the 8th International Conference on Semantic Systems
User-driven relational models for entity-relation search and extraction
Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search
Toward an ecosystem of LOD in the field: LOD content generation and its consuming service
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
A Cognitive Framework for Core Language Understanding and its Computational Implementation
International Journal of Cognitive Informatics and Natural Intelligence
Knowledge base population and visualization using an ontology based on semantic roles
Proceedings of the 2013 workshop on Automated knowledge base construction
Statistical relational data integration for information extraction
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
Tailoring the automated construction of large-scale taxonomies using the web
Language Resources and Evaluation
Coupling as Strategy for Reducing Concept-Drift in Never-ending Learning Environments
Fundamenta Informaticae - Cognitive Informatics and Computational Intelligence: Theory and Applications
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A key question regarding the future of the semantic web is "how will we acquire structured information to populate the semantic web on a vast scale?" One approach is to enter this information manually. A second approach is to take advantage of pre-existing databases, and to develop common ontologies, publishing standards, and reward systems to make this data widely accessible. We consider here a third approach: developing software that automatically extracts structured information from unstructured text present on the web. We also describe preliminary results demonstrating that machine learning algorithms can learn to extract tens of thousands of facts to populate a diverse ontology, with imperfect but reasonably good accuracy.