# The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain

arXiv: https://arxiv.org/abs/2006.03039 · HTML: https://mlsanity.com/paper/2006.03039/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 30 of 32)

- [MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction](https://mlsanity.com/paper/2109.15290/) (2021)
- [UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition](https://mlsanity.com/paper/2308.03279/) (2023)
- [MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling](https://mlsanity.com/paper/2305.08264/) (2023)
- [Foundational Large Language Models for Materials Research](https://mlsanity.com/paper/2412.09560/) (2024)
- [PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature](https://mlsanity.com/paper/2311.07715/) (2023)
- [SsciBERT: A Pre-trained Language Model for Social Science Texts](https://mlsanity.com/paper/2206.04510/) (2022)
- [SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains](https://mlsanity.com/paper/2302.06868/) (2023)
- [Cross-Domain Multi-Task Learning for Sequential Sentence Classification in Research Papers](https://mlsanity.com/paper/2102.06008/) (2021)
- [To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning](https://mlsanity.com/paper/2104.08078/) (2021)
- [Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction](https://mlsanity.com/paper/2310.08383/) (2023)
- [CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain](https://mlsanity.com/paper/2112.08754/) (2021)
- [PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text](https://mlsanity.com/paper/2210.12401/) (2022)
- [TADA: Efficient Task-Agnostic Domain Adaptation for Transformers](https://mlsanity.com/paper/2305.12717/) (2023)
- [MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science](https://mlsanity.com/paper/2410.15126/) (2024)
- [A New Entity Extraction Method Based on Machine Reading Comprehension](https://mlsanity.com/paper/2108.06444/) (2021)
- [Detecting Entities in the Astrophysics Literature: A Comparison of Word-based and Span-based Entity Recognition Methods](https://mlsanity.com/paper/2211.13819/) (2022)
- [Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain](https://mlsanity.com/paper/2304.14745/) (2023)
- [MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain](https://mlsanity.com/paper/2307.02340/) (2023)
- [An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains](https://mlsanity.com/paper/2411.14551/) (2024)
- [Incorporating Domain Knowledge into Materials Tokenization](https://mlsanity.com/paper/2506.11115/) (2025)
- [Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting](https://mlsanity.com/paper/2109.08597/) (2021)
- [Multi-Source (Pre-)Training for Cross-Domain Measurement, Unit and Context Extraction](https://mlsanity.com/paper/2308.02951/) (2023)
- [MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain](https://mlsanity.com/paper/2310.15569/) (2023)
- [Quokka: An Open-source Large Language Model ChatBot for Material Science](https://mlsanity.com/paper/2401.01089/) (2024)
- [KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistry](https://mlsanity.com/paper/2409.18695/) (2024)
- [Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers](https://mlsanity.com/paper/2102.00819/) (2021)
- [ULSA: Unified Language of Synthesis Actions for Representation of Synthesis Protocols](https://mlsanity.com/paper/2201.09329/) (2022)
- [MIST: a Large-Scale Annotated Resource and Neural Models for Functions of Modal Verbs in English Scientific Text](https://mlsanity.com/paper/2212.07156/) (2022)
- [MaterioMiner -- An ontology-based text mining dataset for extraction of process-structure-property entities](https://mlsanity.com/paper/2408.04661/) (2024)
- [Can AI Extract Antecedent Factors of Human Trust in AI? An Application of Information Extraction for Scientific Literature in Behavioural and Computer Sciences](https://mlsanity.com/paper/2412.11344/) (2024)

## Abstract (arXiv)

This paper presents a new challenging information extraction task in the domain of materials science. We develop an annotation scheme for marking information on experiments related to solid oxide fuel cells in scientific publications, such as involved materials and measurement conditions. With this paper, we publish our annotation guidelines, as well as our SOFC-Exp corpus consisting of 45 open-access scholarly articles annotated by domain experts. A corpus and an inter-annotator agreement study demonstrate the complexity of the suggested named entity recognition and slot filling tasks as well as high annotation quality. We also present strong neural-network based models for a variety of tasks that can be addressed on the basis of our new data set. On all tasks, using BERT embeddings leads to large performance gains, but with increasing task complexity, adding a recurrent neural network on top seems beneficial. Our models will serve as competitive baselines in future work, and analysis of their performance highlights difficult cases when modeling the data and suggests promising research directions.