Sentence simplification for semantic role labelling and information extraction
Abstract
In this paper, we report on the extrinsic evaluation of an automatic sentence simplification method with respect to two NLP tasks: semantic role labelling (SRL) and information extraction (IE). The paper begins with our observation of challenges in the intrinsic evaluation of sentence simplification systems, which motivates the use of extrinsic evaluation of these systems with respect to other NLP tasks. We describe the two NLP systems and the test data used in the extrinsic evaluation, and present arguments and evidence motivating the integration of a sentence simplification step as a means of improving the accuracy of these systems. Our evaluation reveals that their performance is improved by the simplification step: the SRL system is better able to assign semantic roles to the majority of the arguments of verbs and the IE system is better able to identify fillers for all IE template slots.Citation
Evans, R. and Orasan, C. (2019) Sentence simplification for semantic role labelling and information extraction, RANLP 2019, 2nd-4th September 2019, Varna, Bulgaria, pp. 285-294.Publisher
RANLPAdditional Links
http://lml.bas.bg/ranlp2019/proceedings-ranlp-2019.pdfType
Conference contributionLanguage
enISSN
1313-8502ISBN
9789544520557
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/