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
Collective information extraction with relational Markov networks
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
An effective two-stage model for exploiting non-local dependencies in named entity recognition
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Foundations and Trends in Databases
Graph construction and b-matching for semi-supervised learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Answering table augmentation queries from unstructured lists on the web
Proceedings of the VLDB Endowment
Harvesting relational tables from lists on the web
Proceedings of the VLDB Endowment
Coupled semi-supervised learning for information extraction
Proceedings of the third ACM international conference on Web search and data mining
Convergent message passing algorithms: a unifying view
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Learning with scope, with application to information extraction and classification
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Rich prior knowledge in learning for NLP
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
WebSets: extracting sets of entities from the web using unsupervised information extraction
Proceedings of the fifth ACM international conference on Web search and data mining
Knowledge harvesting in the big-data era
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Data-based research at IIT Bombay
ACM SIGMOD Record
Synthesizing union tables from the web
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Acquisition of open-domain classes via intersective semantics
Proceedings of the 23rd international conference on World wide web
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We consider the problem of jointly training structured models for extraction from multiple web sources whose records enjoy partial content overlap. This has important applications in open-domain extraction, e.g. a user materializing a table of interest from multiple relevant unstructured sources; or a site like Freebase augmenting an incomplete relation by extracting more rows from web sources. Such applications require extraction over arbitrary domains, so one cannot use a pre-trained extractor or demand a huge labeled dataset. We propose to overcome this lack of supervision by using content overlap across the related web sources. Existing methods of exploiting overlap have been developed under settings that do not generalize easily to the scale and diversity of overlap seen on Web sources. We present an agreement-based learning framework that jointly trains the models by biasing them to agree on the agreement regions, i.e. shared text segments. We present alternatives within our framework to trade-off tractability, robustness to noise, and extent of agreement enforced; and propose a scheme of partitioning agreement regions that leads to efficient training while maximizing overall accuracy. Further, we present a principled scheme to discover low-noise agreement regions in unlabeled data across multiple sources. Through extensive experiments over 58 different extraction domains, we establish that our framework provides significant boosts over uncoupled training, and scores over alternatives such as collective inference, staged training, and multi-view learning.