# MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction arXiv: https://arxiv.org/abs/2401.09839 ยท HTML: https://mlsanity.com/paper/2401.09839/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 65 references that are in the map. - First posted on arXiv 18 Jan 2024. ## Builds on (most cited first, 30 of 30) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019) - [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015) - [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015) - [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015) - [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018) - [One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling](https://mlsanity.com/paper/1312.3005/) (2013) - [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019) - [A Simple Method for Commonsense Reasoning](https://mlsanity.com/paper/1806.02847/) (2018) - [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016) - [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017) - [MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction](https://mlsanity.com/paper/2109.15290/) (2021) - [Attention Guided Graph Convolutional Networks for Relation Extraction](https://mlsanity.com/paper/1906.07510/) (2019) - [Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction](https://mlsanity.com/paper/1911.09886/) (2019) - [Joint entity recognition and relation extraction as a multi-head selection problem](https://mlsanity.com/paper/1804.07847/) (2018) - [Neural Open Information Extraction](https://mlsanity.com/paper/1805.04270/) (2018) - [RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://mlsanity.com/paper/1812.04361/) (2018) - [Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention](https://mlsanity.com/paper/1804.06987/) (2018) - [CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases](https://mlsanity.com/paper/1610.08763/) (2016) - [Deep Graph Convolutional Encoders for Structured Data to Text Generation](https://mlsanity.com/paper/1810.09995/) (2018) - [Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions](https://mlsanity.com/paper/1904.00143/) (2019) - [End-to-end neural relation extraction using deep biaffine attention](https://mlsanity.com/paper/1812.11275/) (2018) - [MatScIE: An automated tool for the generation of databases of methods and parameters used in the computational materials science literature](https://mlsanity.com/paper/2009.06819/) (2020) - [Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages](https://mlsanity.com/paper/2302.09685/) (2023) - [Fine-grained Intent Classification in the Legal Domain](https://mlsanity.com/paper/2205.03509/) (2022) - [A Question-Focused Multi-Factor Attention Network for Question Answering](https://mlsanity.com/paper/1801.08290/) (2018) - [RTE: A Tool for Annotating Relation Triplets from Text](https://mlsanity.com/paper/2108.08184/) (2021) ## Built on by (most cited first, 11 of 11) - [Foundational Large Language Models for Materials Research](https://mlsanity.com/paper/2412.09560/) (2024) - [LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation](https://mlsanity.com/paper/2510.23040/) (2025) - [Mining experimental data from Materials Science literature with Large Language Models: an evaluation study](https://mlsanity.com/paper/2401.11052/) (2024) - [Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection](https://mlsanity.com/paper/2508.16122/) (2025) - [Intent Detection and Entity Extraction from BioMedical Literature](https://mlsanity.com/paper/2404.03598/) (2024) - [MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science](https://mlsanity.com/paper/2410.15126/) (2024) - [On The Persona-based Summarization of Domain-Specific Documents](https://mlsanity.com/paper/2406.03986/) (2024) - [A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents](https://mlsanity.com/paper/2410.22476/) (2024) - [Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents](https://mlsanity.com/paper/2509.10935/) (2025) - [IDALC: A Semi-Supervised Framework for Intent Detection and Active Learning based Correction](https://mlsanity.com/paper/2511.05921/) (2025) - [Sustainable Materials Discovery in the Era of Artificial Intelligence](https://mlsanity.com/paper/2601.21527/) (2026) ## Abstract (arXiv) Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc. Automatically extracting this information to generate a material science knowledge base is a challenging task. In this paper, we propose MatSciRE (Material Science Relation Extractor), a Pointer Network-based encoder-decoder framework, to jointly extract entities and relations from material science articles as a triplet (entity1, relation, entity2). Specifically, we target the battery materials and identify five relations to work on - conductivity, coulombic efficiency, capacity, voltage, and energy. Our proposed approach achieved a much better F1-score (0.771) than a previous attempt using ChemDataExtractor (0.716). The overall graphical framework of MatSciRE is shown in Fig 1. The material information is extracted from material science literature in the form of entity-relation triplets using MatSciRE.