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://aclanthology.org/D17-1279
- DOI:
- 10.18653/v1/D17-1279
- Cite (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.
- Cite (Informal):
- Scientific Information Extraction with Semi-supervised Neural Tagging (Luan et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/D17-1279.pdf