Scientific Information Extraction with Semi-supervised Neural Tagging

Yi Luan, Mari Ostendorf, Hannaneh Hajishirzi


Abstract
This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.
Anthology ID:
D17-1279
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2641–2651
Language:
URL:
https://www.aclweb.org/anthology/D17-1279
DOI:
10.18653/v1/D17-1279
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1279.pdf