Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering word senses from text
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Web-scale information extraction in knowitall: (preliminary results)
Proceedings of the 13th international conference on World Wide Web
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Acquisition of categorized named entities for web search
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Synonymous collocation extraction using translation information
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Collective information extraction with relational Markov networks
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Preemptive information extraction using unrestricted relation discovery
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Clustering for unsupervised relation identification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Weakly-supervised discovery of named entities using web search queries
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
"More like these": growing entity classes from seeds
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Extracting Semantic Networks from Text Via Relational Clustering
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Language-Independent Set Expansion of Named Entities Using the Web
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Weakly-supervised acquisition of labeled class instances using graph random walks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Semi-automatic entity set refinement
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
Helping editors choose better seed sets for entity set expansion
Proceedings of the 18th ACM conference on Information and knowledge management
Automatic set instance extraction using the web
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Employing topic models for pattern-based semantic class discovery
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Entity extraction via ensemble semantics
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Web-scale distributional similarity and entity set expansion
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Open information extraction using Wikipedia
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
An active learning approach to finding related terms
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Corpus-based semantic class mining: distributional vs. pattern-based approaches
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Global learning of typed entailment rules
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Nonlinear evidence fusion and propagation for hyponymy relation mining
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Filtering and clustering relations for unsupervised information extraction in open domain
Proceedings of the 20th ACM international conference on Information and knowledge management
Structured relation discovery using generative models
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Aligning needles in a haystack: paraphrase acquisition across the web
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Integrating syntactic and semantic analysis into the open information extraction paradigm
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Discovering significant types of relations from the web is challenging because of its open nature. Unsupervised algorithms are developed to extract relations from a corpus without knowing the relations in advance, but most of them rely on tagging arguments of predefined types. Recently, a new algorithm was proposed to jointly extract relations and their argument semantic classes, taking a set of relation instances extracted by an open IE algorithm as input. However, it cannot handle polysemy of relation phrases and fails to group many similar ("synonymous") relation instances because of the sparseness of features. In this paper, we present a novel unsupervised algorithm that provides a more general treatment of the polysemy and synonymy problems. The algorithm incorporates various knowledge sources which we will show to be very effective for unsupervised extraction. Moreover, it explicitly disambiguates polysemous relation phrases and groups synonymous ones. While maintaining approximately the same precision, the algorithm achieves significant improvement on recall compared to the previous method. It is also very efficient. Experiments on a real-world dataset show that it can handle 14.7 million relation instances and extract a very large set of relations from the web.