Scientific Information Extraction with Semi-supervised Neural Tagging
This work addresses information extraction for scientific articles, an incremental improvement in a domain-specific area.
The paper tackles the problem of extracting and categorizing keyphrases from scientific articles, achieving state-of-the-art performance on the 2017 SemEval Task 10 ScienceIE task with semi-supervised neural tagging methods.
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.