Sentence Simplification for Semantic Role Labelling and Information Extraction

Richard Evans, Constantin Orasan


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.
Anthology ID:
R19-1033
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
285–294
Language:
URL:
https://aclanthology.org/R19-1033
DOI:
10.26615/978-954-452-056-4_033
Bibkey:
Cite (ACL):
Richard Evans and Constantin Orasan. 2019. Sentence Simplification for Semantic Role Labelling and Information Extraction. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 285–294, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Sentence Simplification for Semantic Role Labelling and Information Extraction (Evans & Orasan, RANLP 2019)
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PDF:
https://preview.aclanthology.org/auto-file-uploads/R19-1033.pdf