# PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature

arXiv: https://arxiv.org/abs/2311.07715 · HTML: https://mlsanity.com/paper/2311.07715/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 13 Nov 2023.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [ToolQA: A Dataset for LLM Question Answering with External Tools](https://mlsanity.com/paper/2306.13304/) (2023)
- [On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines](https://mlsanity.com/paper/2006.04884/) (2020)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://mlsanity.com/paper/2306.15895/) (2023)
- [Does Synthetic Data Generation of LLMs Help Clinical Text Mining?](https://mlsanity.com/paper/2303.04360/) (2023)
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://mlsanity.com/paper/2005.00512/) (2020)
- [A General Framework for Information Extraction using Dynamic Span Graphs](https://mlsanity.com/paper/1904.03296/) (2019)
- [Document-Level N-ary Relation Extraction with Multiscale Representation Learning](https://mlsanity.com/paper/1904.02347/) (2019)
- [Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction](https://mlsanity.com/paper/1906.09317/) (2019)
- [The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain](https://mlsanity.com/paper/2006.03039/) (2020)
- [CitationIE: Leveraging the Citation Graph for Scientific Information Extraction](https://mlsanity.com/paper/2106.01560/) (2021)
- [ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select](https://mlsanity.com/paper/2210.14427/) (2022)
- [A Dataset for N-ary Relation Extraction of Drug Combinations](https://mlsanity.com/paper/2205.02289/) (2022)

## Built on by (most cited first, 13 of 13)

- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records](https://mlsanity.com/paper/2401.07128/) (2024)
- [HYDRA: Model Factorization Framework for Black-Box LLM Personalization](https://mlsanity.com/paper/2406.02888/) (2024)
- [A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction](https://mlsanity.com/paper/2402.13364/) (2024)
- [MatViX: Multimodal Information Extraction from Visually Rich Articles](https://mlsanity.com/paper/2410.20494/) (2024)
- [TritonDFT: Automating DFT with a Multi-Agent Framework](https://mlsanity.com/paper/2603.03372/) (2026)
- [MSQA: Benchmarking LLMs on Graduate-Level Materials Science Reasoning and Knowledge](https://mlsanity.com/paper/2505.23982/) (2025)
- [Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing](https://mlsanity.com/paper/2402.19462/) (2024)
- [TextMineX: Data, Evaluation Framework and Ontology-guided LLM Pipeline for Humanitarian Mine Action](https://mlsanity.com/paper/2509.15098/) (2025)
- [From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cycle](https://mlsanity.com/paper/2510.27369/) (2025)
- [Retrieval Augmented Generation of Literature-derived Polymer Knowledge: The Example of a Biodegradable Polymer Expert System](https://mlsanity.com/paper/2602.16650/) (2026)
- [Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SCILIRE System](https://mlsanity.com/paper/2603.12638/) (2026)
- [Exploring LLMs and RAG for Plausible and Explainable Material Prediction of Vehicle Components](https://mlsanity.com/paper/2609.18437/) (2026)

## Abstract (arXiv)

Scientific information extraction (SciIE), which aims to automatically extract information from scientific literature, is becoming more important than ever. However, there are no existing SciIE datasets for polymer materials, which is an important class of materials used ubiquitously in our daily lives. To bridge this gap, we introduce POLYIE, a new SciIE dataset for polymer materials. POLYIE is curated from 146 full-length polymer scholarly articles, which are annotated with different named entities (i.e., materials, properties, values, conditions) as well as their N-ary relations by domain experts. POLYIE presents several unique challenges due to diverse lexical formats of entities, ambiguity between entities, and variable-length relations. We evaluate state-of-the-art named entity extraction and relation extraction models on POLYIE, analyze their strengths and weaknesses, and highlight some difficult cases for these models. To the best of our knowledge, POLYIE is the first SciIE benchmark for polymer materials, and we hope it will lead to more research efforts from the community on this challenging task. Our code and data are available on: https://github.com/jerry3027/PolyIE.