DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
A novel use of statistical parsing to extract information from text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A composite kernel to extract relations between entities with both flat and structured features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
Multiscale analysis of document corpora based on diffusion models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Distant supervision for relation extraction without labeled data
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 2 - Volume 2
Multi-task transfer learning for weakly-supervised relation extraction
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 2 - Volume 2
Convolution kernels on constituent, dependency and sequential structures for relation extraction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Learning 5000 relational extractors
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Exploiting background knowledge for relation extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Kernel-based semantic relation detection and classification via enriched parse tree structure
Journal of Computer Science and Technology - Special issue on natural language processing
Reducing wrong labels in distant supervision for relation extraction
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Pattern learning for relation extraction with a hierarchical topic model
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Knowledge harvesting in the big-data era
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Relation extraction and scoring in DeepQA
IBM Journal of Research and Development
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This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge learned from previously trained relation detectors. Specifically, we detect a new semantic relation by projecting the new relation's training instances onto a lower dimension topic space constructed from existing relation detectors through a three step process. First, we construct a large relation repository of more than 7,000 relations from Wikipedia. Second, we construct a set of non-redundant relation topics defined at multiple scales from the relation repository to characterize the existing relations. Similar to the topics defined over words, each relation topic is an interpretable multinomial distribution over the existing relations. Third, we integrate the relation topics in a kernel function, and use it together with SVM to construct detectors for new relations. The experimental results on Wikipedia and ACE data have confirmed that background-knowledge-based topics generated from the Wikipedia relation repository can significantly improve the performance over the state-of-the-art relation detection approaches.