Context-Aware Text Re-Ranking through Unsupervised Manifold Learning
Resumo
Information Retrieval (IR) enables efficient knowledge discovery within large document collections. Despite the evolution from sparse lexical models such as BM25 to dense neural embeddings, retrieval ranking still relies predominantly on independent query–document scoring, and most re-ranking strategies require supervised relevance signals. In image retrieval, unsupervised methods that exploit manifold structure and contextual neighborhood relationships have proven highly effective, yet their transfer to text remains unexplored. This study investigates whether such rank-based contextual re-ranking techniques can enhance text retrieval without any modality-specific adaptation. We present a systematic pipeline integrating data collection, sparse and dense retrieval, contextual re-ranking, and effectiveness analysis, and conduct 972 experiments spanning three unsupervised methods across nine diverse public datasets and four retrieval models. Results measured by MAP, Precision, and Recall show consistent improvements over baseline retrievers, with relative Precision@20 gains reaching up to +19.8%. These findings support the hypothesis that unsupervised contextual re-ranking can significantly improve text retrieval effectiveness and suggest that the underlying techniques are modality-agnostic.
Palavras-chave:
Information Retrival, Re-Ranking, Unsupervised
Referências
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Burges, C. J. (2010). From ranknet to lambdarank to lambdamart: An overview. Learning, 11(23-581):81.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 4171–4186.
Guimarães Pedronette, D. C., Pascotti Valem, L., and Latecki, L. J. (2021). Efficient rank-based diffusion process with assured convergence. Journal of Imaging, 7(3).
Izacard, G., Caron, M., Hosseini, L., Riedel, S., Bojanowski, P., Joulin, A., and Grave, E. (2021). Unsupervised dense information retrieval with contrastive learning. arXiv preprint arXiv:2112.09118.
Joachims, T. (2002). Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 133–142.
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., and Yih, W.-t. (2020). Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pages 6769–6781.
Lavrenko, V. and Croft, W. B. (2003). Relevance models in information retrieval. In Language modeling for information retrieval, pages 11–56. Springer.
Liu, T.-Y. (2009). Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval, 3(3):225–331.
Nogueira, R. and Cho, K. (2019). Passage re-ranking with bert. arXiv preprint arXiv:1901.04085.
Pedronette, D. C. G., Valem, L. P., and da S. Torres, R. (2021). A bfs-tree of ranking references for unsupervised manifold learning. Pattern Recognition, 111:107666.
Pisani, F., Pascotti Valem, L., Guimarães Pedronette, D. C., da S. Torres, R., Borin, E., and Breternitz Jr., M. (2020). A unified model for accelerating unsupervised iterative re-ranking algorithms. Concurrency and Computation: Practice and Experience, 32(14):e5702.
Pisani, F., Pedronette, D. C. G., Torres, R. d. S., and Borin, E. (2017). Contextual spaces re-ranking: accelerating the re-sort ranked lists step on heterogeneous systems. Concurrency and Computation: Practice and Experience, 29(22):e3962. e3962 cpe.3962.
Robertson, S., Walker, S., Jones, S., Hancock-Beaulieu, M., and Gatford, M. (1994). Okapi at trec-3. pages 0–.
Rocchio Jr, J. J. (1971). Relevance feedback in information retrieval. The SMART retrieval system: experiments in automatic document processing.
Salton, G. and Buckley, C. (1990). Improving retrieval performance by relevance feedback. Journal of the American society for information science, 41(4):288–297.
Valem, L. P. and Guimarães Pedronette, D. C. (2016). Unsupervised similarity learning through cartesian product of ranking references for image retrieval tasks. In 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pages 249–256.
Valem, L. P., Pedronette, D. C. G. a., Torres, R. d. S., Borin, E., and Almeida, J. (2015). Effective, efficient, and scalable unsupervised distance learning in image retrieval tasks. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, ICMR ’15, page 51–58, New York, NY, USA. Association for Computing Machinery.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I. (2017). Attention is all you need. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
Xu, Z., Mo, F., Huang, Z., Zhang, C., Yu, P., Wang, B., Lin, J., and Srikumar, V. (2025). A survey of model architectures in information retrieval. arXiv preprint arXiv:2502.14822.
Zerveas, G., Rekabsaz, N., Cohen, D., and Eickhoff, C. (2022). Coder: An efficient framework for improving retrieval through contextual document embedding reranking. In Proceedings of the 2022 conference on empirical methods in natural language processing, pages 10626–10644.
Zerveas, G., Rekabsaz, N., and Eickhoff, C. (2023). Enhancing the ranking context of dense retrieval through reciprocal nearest neighbors. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10779–10803.
Zhao, W. X., Liu, J., Ren, R., and Wen, J.-R. (2024). Dense text retrieval based on pretrained language models: A survey. ACM Trans. Inf. Syst., 42(4).
Aizawa, A. (2003). An information-theoretic perspective of tf–idf measures. Information Processing Management, 39(1):45–65.
Baeza-Yates, R., Ribeiro-Neto, B., et al. (1999). Modern information retrieval, volume 463. ACM press New York.
Burges, C. J. (2010). From ranknet to lambdarank to lambdamart: An overview. Learning, 11(23-581):81.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 4171–4186.
Guimarães Pedronette, D. C., Pascotti Valem, L., and Latecki, L. J. (2021). Efficient rank-based diffusion process with assured convergence. Journal of Imaging, 7(3).
Izacard, G., Caron, M., Hosseini, L., Riedel, S., Bojanowski, P., Joulin, A., and Grave, E. (2021). Unsupervised dense information retrieval with contrastive learning. arXiv preprint arXiv:2112.09118.
Joachims, T. (2002). Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 133–142.
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., and Yih, W.-t. (2020). Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pages 6769–6781.
Lavrenko, V. and Croft, W. B. (2003). Relevance models in information retrieval. In Language modeling for information retrieval, pages 11–56. Springer.
Liu, T.-Y. (2009). Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval, 3(3):225–331.
Nogueira, R. and Cho, K. (2019). Passage re-ranking with bert. arXiv preprint arXiv:1901.04085.
Pedronette, D. C. G., Valem, L. P., and da S. Torres, R. (2021). A bfs-tree of ranking references for unsupervised manifold learning. Pattern Recognition, 111:107666.
Pisani, F., Pascotti Valem, L., Guimarães Pedronette, D. C., da S. Torres, R., Borin, E., and Breternitz Jr., M. (2020). A unified model for accelerating unsupervised iterative re-ranking algorithms. Concurrency and Computation: Practice and Experience, 32(14):e5702.
Pisani, F., Pedronette, D. C. G., Torres, R. d. S., and Borin, E. (2017). Contextual spaces re-ranking: accelerating the re-sort ranked lists step on heterogeneous systems. Concurrency and Computation: Practice and Experience, 29(22):e3962. e3962 cpe.3962.
Robertson, S., Walker, S., Jones, S., Hancock-Beaulieu, M., and Gatford, M. (1994). Okapi at trec-3. pages 0–.
Rocchio Jr, J. J. (1971). Relevance feedback in information retrieval. The SMART retrieval system: experiments in automatic document processing.
Salton, G. and Buckley, C. (1990). Improving retrieval performance by relevance feedback. Journal of the American society for information science, 41(4):288–297.
Valem, L. P. and Guimarães Pedronette, D. C. (2016). Unsupervised similarity learning through cartesian product of ranking references for image retrieval tasks. In 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pages 249–256.
Valem, L. P., Pedronette, D. C. G. a., Torres, R. d. S., Borin, E., and Almeida, J. (2015). Effective, efficient, and scalable unsupervised distance learning in image retrieval tasks. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, ICMR ’15, page 51–58, New York, NY, USA. Association for Computing Machinery.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I. (2017). Attention is all you need. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
Xu, Z., Mo, F., Huang, Z., Zhang, C., Yu, P., Wang, B., Lin, J., and Srikumar, V. (2025). A survey of model architectures in information retrieval. arXiv preprint arXiv:2502.14822.
Zerveas, G., Rekabsaz, N., Cohen, D., and Eickhoff, C. (2022). Coder: An efficient framework for improving retrieval through contextual document embedding reranking. In Proceedings of the 2022 conference on empirical methods in natural language processing, pages 10626–10644.
Zerveas, G., Rekabsaz, N., and Eickhoff, C. (2023). Enhancing the ranking context of dense retrieval through reciprocal nearest neighbors. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10779–10803.
Zhao, W. X., Liu, J., Ren, R., and Wen, J.-R. (2024). Dense text retrieval based on pretrained language models: A survey. ACM Trans. Inf. Syst., 42(4).
Publicado
08/09/2026
Como Citar
VENEZIAN, Luis F. A.; PEDRONETTE, Daniel Carlos Guimarães.
Context-Aware Text Re-Ranking through Unsupervised Manifold Learning. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 127-140.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2026.249153.
