Information extraction, data mining and joint inference
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Using semantic relations to refine coreference decisions
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Experiments with reasoning for temporal relations between events
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Jointly combining implicit constraints improves temporal ordering
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Predicting unknown time arguments based on cross-event propagation
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Joint inference for knowledge extraction from biomedical literature
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Using document level cross-event inference to improve event extraction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Using cross-entity inference to improve event extraction
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Two multivariate generalizations of pointwise mutual information
DiSCo '11 Proceedings of the Workshop on Distributional Semantics and Compositionality
Free-form text summarization in social sensing
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Analysis and refinement of cross-lingual entity linking
CLEF'12 Proceedings of the Third international conference on Information Access Evaluation: multilinguality, multimodality, and visual analytics
Structured exploration of who, what, when, and where in heterogeneous multimedia news sources
Proceedings of the 21st ACM international conference on Multimedia
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Previous information extraction (IE) systems are typically organized as a pipeline architecture of separated stages which make independent local decisions. When the data grows beyond some certain size, the extracted facts become inter-dependent and thus we can take advantage of information redundancy to conduct reasoning across documents and improve the performance of IE. We describe a joint inference approach based on information network structure to conduct cross-fact reasoning with an integer linear programming framework. Without using any additional labeled data this new method obtained 13.7%-24.4% user browsing cost reduction over a state-of-the-art IE system which extracts various types of facts independently.