An Introduction to Variational Methods for Graphical Models
Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
2D Conditional Random Fields for Web information extraction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Entity Resolution with Markov Logic
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
The Journal of Machine Learning Research
A probabilistic graphical model for joint answer ranking in question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Webpage understanding: an integrated approach
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction
The Journal of Machine Learning Research
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Towards combining web classification and web information extraction: a case study
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Chinese named entity recognition with cascaded hybrid model
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Joint inference in information extraction
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Relation extraction from wikipedia using subtree mining
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
An integrated discriminative probabilistic approach to information extraction
Proceedings of the 18th ACM conference on Information and knowledge management
Jointly identifying temporal relations with Markov Logic
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
Semantic relation extraction with kernels over typed dependency trees
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Variational approximations between mean field theory and the junction tree algorithm
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Hi-index | 0.00 |
Most high-level information extraction (IE) consists of compound and aggregated subtasks. Such IE problems are generally challenging and they have generated increasing interest recently. We investigate two representative IE tasks: (1) entity identification and relation extraction from Wikipedia, and (2) citation matching, and we formally define joint optimization of information extraction. We propose a joint paradigm integrating three factors -- segmentation, relation, and segmentation-relation joint factors, to solve all relevant subtasks simultaneously. This modeling offers a natural formalism for exploiting bidirectional rich dependencies and interactions between relevant subtasks to capture mutual benefits. Since exact parameter estimation is prohibitively intractable, we present a general, highly-coupled learning algorithm based on variational expectation maximization (VEM) to perform parameter estimation approximately in a top-down and bottom-up manner, such that information can flow bidirectionally and mutual benefits from different subtasks can be well exploited. In this algorithm, both segmentation and relation are optimized iteratively and collaboratively using hypotheses from each other. We conducted extensive experiments using two real-world datasets to demonstrate the promise of our approach.