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Approximate nearest neighbor for long document relationship labeling in digital libraries

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Abstract

Relationship tagging of long text documents is a growing need in information science, spurred by the emergence of multi-million book bibliographic digital libraries. Large digital libraries offer an unprecedented glimpse into cultural history through their collections, but the combination of collection scale and document length complicates their study, given that prior work on large corpora has dealt primarily with much shorter texts. This study presents and evaluates an approach for fast retrieval on long texts, which leverages a chunk-and-aggregate approach with document sub-units to capture nuanced similarity relationships at scales which are not otherwise tractable. This approach is evaluated on book relationships from the HathiTrust Digital Library and shows strong results for relationships beyond exact duplicates. Finally, we argue for the value of approximate nearest neighbor search for narrowing the search space for downstream classification and retrieval contexts.

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  1. https://github.com/massivetexts/compare-tools.

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Acknowledgements

This work made possible by IMLS grant #LG-86-18-0061-18.

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Authors and Affiliations

  1. University of Denver, Denver, USA

    Peter Organisciak

  2. Nomic AI, New York, USA

    Benjamin M. Schmidt

  3. University of Canterbury, Christchurch, New Zealand

    Matthew Durward

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  1. Peter Organisciak
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Correspondence to Peter Organisciak.

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Organisciak, P., Schmidt, B.M. & Durward, M. Approximate nearest neighbor for long document relationship labeling in digital libraries. Int J Digit Libr 24, 311–325 (2023). https://doi.org/10.1007/s00799-023-00354-5

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