emnlp emnlp2013 emnlp2013-68 emnlp2013-68-reference knowledge-graph by maker-knowledge-mining

68 emnlp-2013-Effectiveness and Efficiency of Open Relation Extraction


Source: pdf

Author: Filipe Mesquita ; Jordan Schmidek ; Denilson Barbosa

Abstract: 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.


reference text