What does this research mean for the field?

The application of Niels Lund's concept of doceme to contemporary information retrieval systems reveals significant insights into the future of documentation and the comprehension of language by machine learning models. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The paper aims to analyze contemporary information retrieval systems using the concept of doceme.

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February 21, 2026Open Access

Docemes and Document Embeddings

Key Points

The paper aims to analyze contemporary information retrieval systems using the concept of doceme.
Theoretical analysis of machine learning models in language comprehension.
Discussion on the semantic term usage in data science versus information science.
Application of Lund's doceme and Ron Day's Documentarity to Upstash Vector dataset.
Highlights capacity issues of machine learning models in understanding language.
Identifies conceptual differences in semantic terminology across disciplines.
Reflects qualitatively on future documentation implications of systems like Upstash Vector.

Abstract

This position paper takes Niels’ Lund concept of doceme and applies it to an analysis of a contemporary information retrieval system for Wikipedia, constructed using the Vector Database system “Upstash Vector.” It begins with a theoretical analysis of the capacity for machine learning models to comprehend language, situating its position within that wide debate. It proceeds to discuss issues between the use of the term “semantic” in an data science vs. information science context. Finally, it applies theories from Lund’s doceme and Ron Day’s Documentarity to an analysis of the Upstash Vector dataset located on hugging face, closing with a qualitative reflection on what such systems indicate for the future of documentation.

Docemes and Document Embeddings

Key Points

Abstract

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