Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Towards the self-annotating web
Proceedings of the 13th international conference on World Wide Web
Natural Language Engineering
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on 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
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Freebase: a collaboratively created graph database for structuring human knowledge
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Learning classifiers from only positive and unlabeled data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Information arbitrage across multi-lingual Wikipedia
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A simple semi-supervised algorithm for named entity recognition
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Who, what, when, where, why?: comparing multiple approaches to the cross-lingual 5W task
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 1 - Volume 1
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
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
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Open information extraction using Wikipedia
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Learning 5000 relational extractors
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Adapting self-training for semantic role labeling
ACLstudent '10 Proceedings of the ACL 2010 Student Research Workshop
Collective cross-document relation extraction without labelled data
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
MENTA: inducing multilingual taxonomies from wikipedia
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Self-annotation for fine-grained geospatial relation extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Very high accuracy and fast dependency parsing is not a contradiction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A cross-lingual annotation projection approach for relation detection
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Modeling relations and their mentions without labeled text
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
End-to-end relation extraction using distant supervision from external semantic repositories
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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We propose crosslingual distant supervision (crosslingual DS) for relation extraction, an approach that automatically extracts labels from a pivot language for labeling one or more target languages. The approach has two benefits compared to standard DS: (i) increased coverage if target language labels are not available; and (ii) higher accuracy of automatically generated labels because noisy labels are eliminated in crosslingual filtering. An evaluation for two relations of different complexity shows that crosslingual DS increases the accuracy of relation extraction. Our approach is language independent; we successfully apply it to four different languages: Chinese, English, French and German.