Semantic Role Labeling for Open Information Extraction

@inproceedings{Christensen2010SemanticRL,
  title={Semantic Role Labeling for Open Information Extraction},
  author={Janara Christensen and Mausam and Stephen Soderland and Oren Etzioni},
  booktitle={HLT-NAACL 2010},
  year={2010}
}
Open Information Extraction is a recent paradigm for machine reading from arbitrary text. In contrast to existing techniques, which have used only shallow syntactic features, we investigate the use of semantic features (semantic roles) for the task of Open IE. We compare TEXTRUNNER (Banko et al., 2007), a state of the art open extractor, with our novel extractor SRL-IE, which is based on UIUC’s SRL system (Punyakanok et al., 2008). We find that SRL-IE is robust to noisy heterogeneous Web data… CONTINUE READING
Highly Cited
This paper has 97 citations. REVIEW CITATIONS

Citations

Publications citing this paper.

97 Citations

01020'11'13'15'17'19
Citations per Year
Semantic Scholar estimates that this publication has 97 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 23 references

Coupling semisupervised learning of categories and relations

Andrew Carlson, Justin Betteridge, Estevam R. Hruschka, Tom M. Mitchell.
Proceedings of the NAACL HLT 2009 Workskop on Semi-supervised Learning for Natural Language Pro- • 2009
View 1 Excerpt

Cafarella . 2006 . Machine reading

Richard Johansson, Pierre Nugues
2008
View 1 Excerpt