# MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain

arXiv: https://arxiv.org/abs/2310.15569 · HTML: https://mlsanity.com/paper/2310.15569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 53 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://mlsanity.com/paper/1904.02099/) (2019)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures](https://mlsanity.com/paper/1905.06939/) (2019)
- [Simpler but More Accurate Semantic Dependency Parsing](https://mlsanity.com/paper/1807.01396/) (2018)
- [Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling](https://mlsanity.com/paper/1805.04787/) (2018)
- [The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain](https://mlsanity.com/paper/2006.03039/) (2020)
- [A Cross-Task Analysis of Text Span Representations](https://mlsanity.com/paper/2006.03866/) (2020)
- [Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling](https://mlsanity.com/paper/1909.09814/) (2019)
- [Applying Occam's Razor to Transformer-Based Dependency Parsing: What Works, What Doesn't, and What is Really Necessary](https://mlsanity.com/paper/2010.12699/) (2020)
- [Automatically Extracting Action Graphs from Materials Science Synthesis Procedures](https://mlsanity.com/paper/1711.06872/) (2017)

## Built on by (most cited first, 1 of 1)

- [From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry](https://mlsanity.com/paper/2605.03205/) (2026)

## Abstract (arXiv)

Keeping track of all relevant recent publications and experimental results for a research area is a challenging task. Prior work has demonstrated the efficacy of information extraction models in various scientific areas. Recently, several datasets have been released for the yet understudied materials science domain. However, these datasets focus on sub-problems such as parsing synthesis procedures or on sub-domains, e.g., solid oxide fuel cells. In this resource paper, we present MuLMS, a new dataset of 50 open-access articles, spanning seven sub-domains of materials science. The corpus has been annotated by domain experts with several layers ranging from named entities over relations to frame structures. We present competitive neural models for all tasks and demonstrate that multi-task training with existing related resources leads to benefits.