WordNet: a lexical database for English
Communications of the ACM
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Relational learning of pattern-match rules for information extraction
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
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
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Background and overview for KDD Cup 2002 task 1: information extraction from biomedical articles
ACM SIGKDD Explorations Newsletter
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
TEG: a hybrid approach to information extraction
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Automatic pattern acquisition for Japanese information extraction
HLT '01 Proceedings of the first international conference on Human language technology research
Description of the UMass system as used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
Unsupervised named-entity extraction from the web: an experimental study
Artificial Intelligence
Exploiting strong syntactic heuristics and co-training to learn semantic lexicons
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A bootstrapping method for learning semantic lexicons using extraction pattern contexts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Learning to parse database queries using inductive logic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Unsupervised relation extraction by mining Wikipedia texts using information from the web
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
Probabilistic matrix factorization leveraging contexts for unsupervised relation extraction
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
A robust web personal name information extraction system
Expert Systems with Applications: An International Journal
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Most information extraction systems either use hand written extraction patterns or use a machine learning algorithm that is trained on a manually annotated corpus. Both of these approaches require massive human effort and hence prevent information extraction from becoming more widely applicable. In this paper we present URES (Unsupervised Relation Extraction System), which extracts relations from the Web in a totally unsupervised way. It takes as input the descriptions of the target relations, which include the names of the predicates, the types of their attributes, and several seed instances of the relations. Then the system downloads from the Web a large collection of pages that are likely to contain instances of the target relations. From those pages, utilizing the known seed instances, the system learns the relation patterns, which are then used for extraction. We present several experiments in which we learn patterns and extract instances of a set of several common IE relations, comparing several pattern learning and filtering setups. We demonstrate that using simple noun phrase tagger is sufficient as a base for accurate patterns. However, having a named entity recognizer, which is able to recognize the types of the relation attributes significantly, enhances the extraction performance. We also compare our approach with KnowItAll's fixed generic patterns.