mlsanityData to 14 Oct 2026

MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction

arXiv 2401.09839 ↗First posted 18 Jan 2024cs.CL cs.CE cs.IRcited by 11

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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.

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

30 of 65 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 4; 2026: 1.

11 papers.

  1. 2024Foundational Large Language Models for Materials Researchcited by 18
  2. 2025LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generationcited by 13
  3. 2024Mining experimental data from Materials Science literature with Large Language Models: an evaluation studycited by 11
  4. 2025Text Takes Over: A Study of Modality Bias in Multimodal Intent Detectioncited by 7
  5. 2024Intent Detection and Entity Extraction from BioMedical Literaturecited by 6
  6. 2024MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Sciencecited by 5
  7. 2024On The Persona-based Summarization of Domain-Specific Documentscited by 4
  8. 2024A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intentscited by 3
  9. 2025Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documentscited by 1
  10. 2025IDALC: A Semi-Supervised Framework for Intent Detection and Active Learning based Correctioncited by 0
  11. 2026Sustainable Materials Discovery in the Era of Artificial Intelligencecited by 0