Transformer-Based Language Models for Contextual Information Extraction From Scientific Documents

Aino Virtanen

Transformer-Based Language Models for Contextual Information Extraction From Scientific Documents

Author(s)	Aino Virtanen
Country	Finland
Abstract	Scientific literature contains substantial quantities of specialized information concerning methods, materials, datasets, processes, findings, and relationships among research concepts. Automated extraction of this information is challenging because scientific terminology is highly domain-specific and the interpretation of individual expressions frequently depends on surrounding textual context. Conventional information-extraction approaches often rely on manually engineered features or fixed word representations, which may inadequately represent context-dependent meanings. Transformer-based language models provide an alternative by producing contextualized representations in which the interpretation of words and phrases depends on their surrounding linguistic environment. This hypothetical methodological study examines transformer-based language models for contextual information extraction from scientific documents. The proposed framework evaluates named scientific entities and semantic relations using contextual representations. A fictional corpus containing 900 scientific documents is divided into hypothetical training, validation, and testing partitions. Three conceptual extraction configurations are compared: a BiLSTM-CRF baseline using conventional embeddings, a general BERT-based extractor, and a scientific-domain transformer model. The illustrative analysis evaluates entity extraction and relation extraction separately through precision, recall, and F1 score. Under the assigned hypothetical values, the scientific-domain transformer records the strongest entity and relation extraction performance. The improvement is conceptually associated with domain-specific contextual representation rather than with transformer architecture alone. The numerical results are illustrative and are not outputs obtained from implemented models. The study concludes that transformer-based language models provide a useful methodological foundation for scientific information extraction because they can represent context-dependent terminology and relationships more effectively than fixed word representations. However, practical performance depends on annotation quality, disciplinary vocabulary, document length, relation complexity, and model generalization. Reproducible experiments using real annotated scientific corpora are therefore necessary before claims of extraction superiority can be established.
Keywords	Transformer Language Models, Scientific Information Extraction, BERT, SciBERT, Named Entity Recognition, Relation Extraction, Scientific Documents, Contextual Embeddings, Natural Language Processing, Knowledge Extraction.
Field	Engineering
Published In	Volume 1, Issue 4, September-October 2025
Published On	2025-09-15

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Transformer-Based Language Models for Contextual Information Extraction From Scientific Documents

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