Scientific Information Extraction with Semi-supervised Neural Tagging

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Luan, Yi, Ostendorf, Mari, Hannaneh Hajishirzi
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 21.08.2017
Subjects
Algorithms
Computer Science - Computation and Language
Information retrieval
Marking
Scientific papers
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1708.06075