Paper: Effectiveness and Efficiency of Open Relation Extraction

ACL ID D13-1043
Title Effectiveness and Efficiency of Open Relation Extraction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

A large number of Open Relation Extrac- tion approaches have been proposed recently, covering a wide range of NLP machinery, from ?shallow? (e.g., part-of-speech tagging) to ?deep? (e.g., semantic role labeling?SRL). A natural question then is what is the trade- off between NLP depth (and associated com- putational cost) versus effectiveness. This pa- per presents a fair and objective experimental comparison of 8 state-of-the-art approaches over 5 different datasets, and sheds some light on the issue. The paper also describes a novel method, EXEMPLAR, which adapts ideas from SRL to less costly NLP machinery, resulting in substantial gains both in efficiency and ef- fectiveness, over binary and n-ary relation ex- traction tasks.