Scientific Information Extraction with Semi-supervised Neural Tagging

Yi Luan; Mari Ostendorf; Hannaneh Hajishirzi

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arXiv · 1708.06075

Scientific Information Extraction with Semi-supervised Neural Tagging

Yi Luan·Mari Ostendorf·Hannaneh Hajishirzi

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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.

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Yi Luan, Mari Ostendorf, Hannaneh Hajishirzi. 2017-08-21. Scientific Information Extraction with Semi-supervised Neural Tagging. https://arxiv.org/abs/1708.06075

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