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
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Kernel methods for relation extraction
The Journal of Machine Learning Research
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
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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
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
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Measuring the similarity between implicit semantic relations from the web
Proceedings of the 18th international conference on World wide web
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Extracting relations in social networks from the web using similarity between collective contexts
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Exploiting macro and micro relations toward web intelligence
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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Binary semantic relation extraction from Wikipedia is particularly useful for various NLP and Web applications. Currently frequent pattern mining-based methods and syntactic analysis-based methods are two types of leading methods for semantic relation extraction task. With a novel view on integrating syntactic analysis on Wikipedia text with redundancy information from the Web, we propose a multi-view learning approach for bootstrapping relationships between entities with the complementary between the Web view and linguistic view. On the one hand, from the linguistic view, linguistic features are generated from linguistic parsing on Wikipedia texts by abstracting away from different surface realizations of semantic relations. On the other hand, Web features are extracted from the Web corpus to provide frequency information for relation extraction. Experimental evaluation on a relational dataset demonstrates that linguistic analysis on Wikipedia texts and Web collective information reveal different aspects of the nature of entity-related semantic relationships. It also shows that our multi-view learning method considerably boosts the performance comparing to learning with only one view of features, with the weaknesses of one view complement the strengths of the other.