Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Learning to construct knowledge bases from the World Wide Web
Artificial Intelligence - Special issue on Intelligent internet systems
Unsupervised named-entity extraction from the web: an experimental study
Artificial Intelligence
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Web-scale named entity recognition
Proceedings of the 17th ACM conference on Information and knowledge management
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
Unsupervised methods for determining object and relation synonyms on the web
Journal of Artificial Intelligence Research
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Extracting Enterprise Vocabularies Using Linked Open Data
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Coupled semi-supervised learning for information extraction
Proceedings of the third ACM international conference on Web search and data mining
PORE: positive-only relation extraction from wikipedia text
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
A framework for schema-driven relationship discovery from unstructured text
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Unsupervised wrapper induction using linked data
Proceedings of the seventh international conference on Knowledge capture
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We present a technique for reading sentences and producing sets of hypothetical relations that the sentence may be expressing. The technique uses large amounts of instance-level background knowledge about the relations in order to gather statistics on the various ways the relation may be expressed in language, and was inspired by the observation that half of the linguistic forms used to express relations occur very infrequently and are simply not considered by systems that use too few seed examples. Some very early experiments are presented that show promising results.