Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Kernel methods for relation extraction
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
Machine Learning
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
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on 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
Detecting semantic relations between named entities in text using contextual features
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Subtree mining for relation extraction from Wikipedia
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Relation extraction from wikipedia using subtree mining
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Towards a top-down and bottom-up bidirectional approach to joint information extraction
Proceedings of the 20th ACM international conference on Information and knowledge management
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We propose a new integrated approach based on Markov logic networks (MLNs), an effective combination of probabilistic graphical models and first-order logic for statistical relational learning, to extracting relations between entities in encyclopedic articles from Wikipedia. The MLNs model entity relations in a unified undirected graph collectively using multiple features, including contextual, morphological, syntactic, semantic as well as Wikipedia characteristic features which can capture the essential characteristics of relation extraction task. This model makes simultaneous statistical judgments about the relations for a set of related entities. More importantly, implicit relations can also be identified easily. Our experimental results showed that, this integrated probabilistic and logic model significantly outperforms the current state-of-the-art probabilistic model, Conditional Random Fields (CRFs), for relation extraction from encyclopedic articles.