Work

Incorporating Domain Knowledge into Materials Tokenization

Year: 2025

Type: conference-paper

Abstract: While language models are increasingly utilized in materials science, typical models rely on frequency-centric tokenization methods originally developed for natural language processing.However, these methods frequently produce excessive fragmentation and semantic loss, failing to maintain the structural and semantic integrity of material concepts.To address this issue, we propose MATTER, a novel tokenization approach that integrates material knowledge into tokenization.Based on MatDetector trained on our materials knowledge base and a re-ranking method prioritizing material concepts in token merging, MATTER maintains the structural integrity of identified material concepts and prevents fragmentation during tokenization, ensuring their semantic meaning remains intact.The experimental results demonstrate that MATTER outperforms existing tokenization methods, achieving an average performance gain of 4% and 2% in the generation and classification tasks, respectively.These results underscore the importance of domain knowledge for tokenization strategies in scientific text processing. 1

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/2025.acl-long.474

Authors: Yerim Oh, Jun-Hyung Park, Junho Kim, SungHo Kim, SangKeun Lee

Institutions: Korea University, Korea University, Hankuk University of Foreign Studies

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid