AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Learning by reading: a prototype system, performance baseline and lessons learned
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Open information extraction using Wikipedia
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Identifying functional relations in web text
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Unsupervised discovery of domain-specific knowledge from text
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Open information extraction: the second generation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
ClausIE: clause-based open information extraction
Proceedings of the 22nd international conference on World Wide Web
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Building shallow semantic representations from text corpora is the first step to perform more complex tasks such as text entailment, enrichment of knowledge bases, or question answering. Open Information Extraction (OIE) is a recent unsupervised strategy to extract billions of basic assertions from massive corpora, which can be considered as being a shallow semantic representation of those corpora. In this paper, we propose a new multilingual OIE system based on robust and fast rule-based dependency parsing. It permits to extract more precise assertions (verb-based triples) from text than state of the art OIE systems, keeping a crucial property of those systems: scaling to Web-size document collections.