Effectiveness and Efficiency of Open Relation Extraction
Filipe Eduardo Amorim Mesquita, Jordan Schmidek, Denilson Barbosa · 2013
A large number of Open Relation Extraction 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 tradeoff between NLP depth (and associated computational cost) versus effectiveness.This paper 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 effectiveness, over binary and n-ary relation extraction tasks.