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
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Scenario customization for information extraction
Scenario customization for information extraction
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
An improved extraction pattern representation model for automatic IE pattern acquisition
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Understanding the Yarowsky Algorithm
Computational Linguistics
Using the Web to Reduce Data Sparseness in Pattern-Based Information Extraction
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Improving semi-supervised acquisition of relation extraction patterns
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Boosting relation extraction with limited closed-world knowledge
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Dependency graphs as a generic interface between parsers and relation extraction rule learning
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Minimally supervised domain-adaptive parse reranking for relation extraction
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
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The main contribution of this paper is a systematic analysis of a minimally supervised machine learning method for relation extraction grammars. The method is based on a bootstrapping approach in which the bootstrapping is triggered by semantic seeds. The starting point of our analysis is the pattern-learning graph which is a subgraph of the bipartite graph representing all connections between linguistic patterns and relation instances exhibited by the data. It is shown that the performance of such general learning framework for actual tasks is dependent on certain properties of the data and on the selection of seeds. Several experiments have been conducted to gain explanatory insights into the interaction of these two factors. From the investigation of more effective seeds and benevolent data we understand how to improve the learning in less fortunate configurations. A relation extraction method only based on positive examples cannot avoid all false positives, especially when the data properties yield a high recall. Therefore, negative seeds are employed to learn negative patterns, which boost precision.