General bounds on the number of examples needed for learning probabilistic concepts
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
On the sample complexity of noise-tolerant learning
Information Processing Letters
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Web-scale information extraction in knowitall: (preliminary results)
Proceedings of the 13th international conference on World Wide Web
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Automatic acquisition of domain knowledge for Information Extraction
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Gimme' the context: context-driven automatic semantic annotation with C-PANKOW
WWW '05 Proceedings of the 14th international conference on World Wide Web
Unsupervised learning of generalized names
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
CRYSTAL inducing a conceptual dictionary
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Text2Onto: a framework for ontology learning and data-driven change discovery
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Concept-Based search on semi-structured data exploiting mined semantic relations
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
The database research group at the Max-Planck Institute for Informatics
ACM SIGMOD Record
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
How NAGA uncoils: searching with entities and relations
Proceedings of the 16th international conference on World Wide Web
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Webpage understanding: an integrated approach
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Collective knowledge systems: Where the Social Web meets the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
Mining the Web Through Verbs: A Case Study
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Social tags: meaning and suggestions
Proceedings of the 17th ACM conference on Information and knowledge management
Foundations and Trends in Databases
The YAGO-NAGA approach to knowledge discovery
ACM SIGMOD Record
SOFIE: a self-organizing framework for information extraction
Proceedings of the 18th international conference on World wide web
Conceptual Graph Interchange Format for Mining Financial Statements
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
International Journal of Human-Computer Studies
Harvesting and organizing knowledge from the web
ADBIS'07 Proceedings of the 11th East European conference on Advances in databases and information systems
PORE: positive-only relation extraction from wikipedia text
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Querying parse trees of stochastic context-free grammars
Proceedings of the 13th International Conference on Database Theory
From information to knowledge: harvesting entities and relationships from web sources
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Extracting 5W1H event semantic elements from Chinese online news
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Bootstrapping location relations from text
Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in an Information Ecosystem - Volume 47
Information extraction from Wikipedia using pattern learning
Acta Cybernetica
A hybrid approach for the extraction of semantic relations from MEDLINE abstracts
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
PARIS: probabilistic alignment of relations, instances, and schema
Proceedings of the VLDB Endowment
Open language learning for information extraction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Reading the web with learned syntactic-semantic inference rules
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Controlled knowledge base enrichment from web documents
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Learning to predict from textual data
Journal of Artificial Intelligence Research
Journal of Web Engineering
PREDOSE: A semantic web platform for drug abuse epidemiology using social media
Journal of Biomedical Informatics
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The World Wide Web provides a nearly endless source of knowledge, which is mostly given in natural language. A first step towards exploiting this data automatically could be to extract pairs of a given semantic relation from text documents - for example all pairs of a person and her birthdate. One strategy for this task is to find text patterns that express the semantic relation, to generalize these patterns, and to apply them to a corpus to find new pairs. In this paper, we show that this approach profits significantly when deep linguistic structures are used instead of surface text patterns. We demonstrate how linguistic structures can be represented for machine learning, and we provide a theoretical analysis of the pattern matching approach. We show the benefits of our approach by extensive experiments with our prototype system LEILA.