Corpus ID: 10318045

Identifying Relations for Open Information Extraction

@inproceedings{Fader2011IdentifyingRF,
  title={Identifying Relations for Open Information Extraction},
  author={Anthony Fader and S. Soderland and Oren Etzioni},
  booktitle={EMNLP},
  year={2011}
}
Open Information Extraction (IE) is the task of extracting assertions from massive corpora without requiring a pre-specified vocabulary. [...] Key Method We implemented the constraints in the ReVerb Open IE system, which more than doubles the area under the precision-recall curve relative to previous extractors such as TextRunner and woepos. More than 30% of ReVerb's extractions are at precision 0.8 or higher---compared to virtually none for earlier systems. The paper concludes with a detailed analysis of…Expand
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References

SHOWING 1-10 OF 38 REFERENCES
Open Information Extraction Using Wikipedia
TLDR
WOE is presented, an open IE system which improves dramatically on TextRunner's precision and recall and is a novel form of self-supervised learning for open extractors -- using heuristic matches between Wikipedia infobox attribute values and corresponding sentences to construct training data. Expand
Semantic Role Labeling for Open Information Extraction
TLDR
This work investigates the use of semantic features (semantic roles) for the task of Open IE and finds that SRL-IE is robust to noisy heterogeneous Web data and outperforms TextRunner on extraction quality. Expand
The Tradeoffs Between Open and Traditional Relation Extraction
TLDR
A new model for Open IE called O-CRF is presented and it is shown that it achieves increased precision and nearly double the recall than the model employed by TEXTRUNNER, the previous stateof-the-art Open IE system. Expand
Open Information Extraction from the Web
TLDR
Open IE (OIE), a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input, is introduced. Expand
Adapting Open Information Extraction to Domain-Specific Relations
TLDR
The steps needed to adapt Open IE to a domain-specific ontology are explored and the approach of mapping domain-independent tuples to an ontology using domains from DARPA’s Machine Reading Project is demonstrated. Expand
Preemptive Information Extraction using Unrestricted Relation Discovery
TLDR
A technique called Unrestricted Relation Discovery is proposed that discovers all possible relations from texts and presents them as tables in order to extend the boundary of Information Extraction systems. Expand
Learning Information Extraction Rules for Semi-Structured and Free Text
TLDR
WHISK is designed to handle text styles ranging from highly structured to free text, including text that is neither rigidly formatted nor composed of grammatical sentences, and can also handle extraction from free text such as news stories. Expand
A hybrid approach for extracting semantic relations from texts
TLDR
It is suggested that the use of knowledge intensive strategies to process the input text and corpusbased techniques to deal with unpredicted cases and ambiguity problems allows to accurately discover the relevant relations between pairs of entities in that text. Expand
Identifying Functional Relations in Web Text
TLDR
Leibniz is utilized to generate the first public repository of automatically-identified functional relations, exploiting the synergy between the Web corpus and freely-available knowledge resources such as Free-base to solve the challenge of determining whether a textual phrase denotes a functional relation. Expand
Learning 5000 Relational Extractors
TLDR
LUCHS is presented, a self-supervised, relation-specific IE system which learns 5025 relations --- more than an order of magnitude greater than any previous approach --- with an average F1 score of 61%. Expand
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