Communications of the ACM
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Information Extraction: Techniques and Challenges
SCIE '97 International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Unsupervised named-entity extraction from the web: an experimental study
Artificial Intelligence
Web data extraction based on structural similarity
Knowledge and Information Systems
TEG—a hybrid approach to information extraction
Knowledge and Information Systems
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Learning to extract and summarize hot item features from multiple auction web sites
Knowledge and Information Systems
Adaptive information extraction from text by rule induction and generalisation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Distant supervision for relation extraction without labeled data
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 2 - Volume 2
Extracting medication information from discharge summaries
Louhi '10 Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents
Embellishing text search queries to protect user privacy
Proceedings of the VLDB Endowment
QuickView: NLP-based tweet search
ACL '12 Proceedings of the ACL 2012 System Demonstrations
Improving the performance of a named entity recognition system with knowledge acquisition
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
Learning regular expressions to template-based FAQ retrieval systems
Knowledge-Based Systems
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Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional Information Extraction methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many different instances as possible while keeping the precision of the resulting list reasonably high. SRES is a self-supervised Web relation extraction system that learns powerful extraction patterns from unlabeled text, using short descriptions of the target relations and their attributes. SRES automatically generates the training data needed for its pattern-learning component. The performance of SRES is further enhanced by classifying its output instances using the properties of the instances and the patterns. The features we use for classification and the trained classification model are independent from the target relation, which we demonstrate in a series of experiments. We also compare the performance of SRES to the performance of the state-of-the-art KnowItAll system, and to the performance of its pattern learning component, which learns simpler pattern language than SRES.