# Scientific Information Extraction with Semi-supervised Neural Tagging Source: [Scientific Information Extraction with Semi-supervised Neural Tagging]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:45:17.380Z Authors: Yi Luan, Mari Ostendorf, Hannaneh Hajishirzi Published: 2017-08-21T03:33:58.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Span Identification on SemEval ScienceIE Task 10 2017 (test)](): Span Identification; SemEval ScienceIE Task 10 2017 (test); F1 Score: 56.9. - [Keyphrase Extraction on SemEval Task 10 ScienceIE 2017 (test)](): Keyphrase Extraction; SemEval Task 10 ScienceIE 2017 (test); F1 Score: 45.3. ## Original sources and implementation [Original paper]() [PDF]()