Corpus ID: 74065

Open Language Learning for Information Extraction

@inproceedings{Mausam2012OpenLL,
  title={Open Language Learning for Information Extraction},
  author={Mausam and Michael Schmitz and S. Soderland and Robert Bart and Oren Etzioni},
  booktitle={EMNLP-CoNLL},
  year={2012}
}
Open Information Extraction (IE) systems extract relational tuples from text, without requiring a pre-specified vocabulary, by identifying relation phrases and associated arguments in arbitrary sentences. However, state-of-the-art Open IE systems such as ReVerb and woe share two important weaknesses -- (1) they extract only relations that are mediated by verbs, and (2) they ignore context, thus extracting tuples that are not asserted as factual. This paper presents ollie, a substantially… Expand
Open Information Extraction Systems and Downstream Applications
  • Mausam
  • Computer Science
  • IJCAI
  • 2016
Extraction Systems and Downstream Applications
Open Information Extraction
Nested Propositions in Open Information Extraction
Weakly Supervised, Data-Driven Acquisition of Rules for Open Information Extraction
Open Information Extraction with Global Structure Constraints
Open Information Extraction with Tree Kernels
A Language Model for Extracting Implicit Relations
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Open Information Extraction Using Wikipedia
Open Information Extraction from the Web
Open Information Extraction: The Second Generation
An analysis of open information extraction based on semantic role labeling
Learning 5000 Relational Extractors
Distant supervision for relation extraction without labeled data
Combining linguistic and statistical analysis to extract relations from web documents
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