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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.</abstract>
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%0 Conference Proceedings
%T Scientific Information Extraction with Semi-supervised Neural Tagging
%A Luan, Yi
%A Ostendorf, Mari
%A Hajishirzi, Hannaneh
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F luan-etal-2017-scientific
%X 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.
%R 10.18653/v1/D17-1279
%U https://aclanthology.org/D17-1279
%U https://doi.org/10.18653/v1/D17-1279
%P 2641-2651

Markdown (Informal)

[Scientific Information Extraction with Semi-supervised Neural Tagging](https://aclanthology.org/D17-1279) (Luan et al., EMNLP 2017)

• Scientific Information Extraction with Semi-supervised Neural Tagging (Luan et al., EMNLP 2017)

ACL

• Yi Luan, Mari Ostendorf, and Hannaneh Hajishirzi. 2017. Scientific Information Extraction with Semi-supervised Neural Tagging. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2641–2651, Copenhagen, Denmark. Association for Computational Linguistics.