A Branch-and-Bound Approach to Efficient Classification and Retrieval of Documents

Kotaro Ii, Hiroto Saigo, Yasuo Tabei

2024

Abstract

Text classification and retrieval have been crucial tasks in natural language processing. In this paper, we present novel techniques for these tasks by leveraging the invariance of feature order to the evaluation results. Building on the assumption that text retrieval or classification models have already been constructed from the training documents, we propose efficient approaches that can restrict the search space spanned by the test documents. Our approach encompasses two key contributions. The first contribution introduces an efficient method for traversing a search tree, while the second contribution involves the development of novel pruning conditions. Through computational experiments using real-world datasets, we consistently demonstrate that the proposed approach outperforms the baseline method in various scenarios, showcasing its superior speed and efficiency.

Paper Citation

in Harvard Style

Ii K., Saigo H. and Tabei Y. (2024). A Branch-and-Bound Approach to Efficient Classification and Retrieval of Documents. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 205-214. DOI: 10.5220/0012310600003654

in Bibtex Style

@conference{icpram24,
author={Kotaro Ii and Hiroto Saigo and Yasuo Tabei},
title={A Branch-and-Bound Approach to Efficient Classification and Retrieval of Documents},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={205-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012310600003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - A Branch-and-Bound Approach to Efficient Classification and Retrieval of Documents
SN - 978-989-758-684-2
AU - Ii K.
AU - Saigo H.
AU - Tabei Y.
PY - 2024
SP - 205
EP - 214
DO - 10.5220/0012310600003654
PB - SciTePress