# Scientific Information Extraction with Semi-supervised Neural Tagging

arXiv: https://arxiv.org/abs/1708.06075 · HTML: https://mlsanity.com/paper/1708.06075/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2017.

## Built on by (most cited first, 24 of 24)

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- [SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents](https://mlsanity.com/paper/2410.21155/) (2024)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
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- [Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles](https://mlsanity.com/paper/2109.00199/) (2021)
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- [FinRED: A Dataset for Relation Extraction in Financial Domain](https://mlsanity.com/paper/2306.03736/) (2023)
- [Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach](https://mlsanity.com/paper/2311.03401/) (2023)
- [QExplorer: Large Language Model Based Query Extraction for Toxic Content Exploration](https://mlsanity.com/paper/2502.18480/) (2025)
- [Extracting Problem and Method Sentence from Scientific Papers: A Context-enhanced Transformer Using Formulaic Expression Desensitization](https://mlsanity.com/paper/2606.26481/) (2026)
- [Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach](https://mlsanity.com/paper/2608.08636/) (2026)

## Abstract (arXiv)

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.