Krasakis, A. M., Yates, A., & Kanoulas, E. (2024). Contextualizing and Expanding Conversational Queries without Supervision. ACM Transactions on Information Systems, 42(3), Article 77. https://doi.org/10.1145/3632622[details]
Nguyen, T. T., Hendriksen, M. Y., Yates, A. C., & de Rijke, M. (2024). Multimodal Learned Sparse Retrieval with Probabilistic Expansion Control. In Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024 : proceedings (Vol. II, pp. 448–464). ( Lecture Notes in Computer Science; Vol. 14609). Springer. https://doi.org/10.48550/arXiv.2402.17535, https://doi.org/10.1007/978-3-031-56060-6_29
2023
Li, C., Yates, A., Macavaney, S., He, B., & Sun, Y. (2023). PARADE: Passage Representation Aggregation for Document Reranking. ACM Transactions on Information Systems, 42(2), Article 36. https://doi.org/10.1145/3600088
Nguyen, T. T., MacAvaney, S., & Yates, A. C. (2023). A Unified Framework for Learned Sparse Retrieval. In Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023 : proceedings (Vol. III, pp. 101-116). (Lecture Notes in Computer Science; Vol. 13982). Springer. https://doi.org/10.48550/arXiv.2303.13416, https://doi.org/10.1007/978-3-031-28241-6_7
Nguyen, T., MacAvaney, S., & Yates, A. (2023). Adapting Learned Sparse Retrieval for Long Documents. In SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 23-27, 2023, Taipei, Taiwan (pp. 1781-1785). Association for Computing Machinery. https://doi.org/10.1145/3539618.3591943
Pal, V., Yates, A., Kanoulas, E., & de Rijke, M. (2023). MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), The 61st Conference of the Association for Computational Linguistics: Proceedings of the Conference : ACL 2023 : July 9-14, 2023 (Vol. 1, pp. 6322–6334). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.348[details]
Farrell, M. J., Brierley, L., Willoughby, A., Yates, A., & Mideo, N. (2022). Past and future uses of text mining in ecology and evolution. Proceedings of the Royal Society B: Biological Sciences, 289(1975), Article 20212721. Advance online publication. https://doi.org/10.1098/rspb.2021.2721[details]
Khandel, P., Markov, I., Yates, A., & Varbanescu, A-L. (2022). ParClick: A Scalable Algorithm for EM-based Click Models. In WWW'22: proceedings of the ACM Web Conference 2022 : April 25-29, 2022, VIrtual Event, Lyon, France (pp. 392-400). Association for Computing Machinery. https://doi.org/10.1145/3485447.3511967[details]
Krasakis, A. M., Yates, A., & Kanoulas, E. (2022). Zero-shot Query Contextualization for Conversational Search. In SIGIR '22: proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 11-15, 2022, Madrid, Spain (pp. 1880–1884). The Association for Computing Machinery. https://doi.org/10.48550/arXiv.2204.10613, https://doi.org/10.1145/3477495.3531769[details]
Naseri, S., Dalton, J., Yates, A., & Allan, J. (2022). CEQE to SQET: A study of contextualized embeddings for query expansion. Information Retrieval Journal, 25(2), 184–208. https://doi.org/10.1007/s10791-022-09405-y[details]
Nguyen, T., Yates, A., Zirikly, A., Desmet, B., & Cohan, A. (2022). Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires. In S. Muresan, P. Nakov, & A. Villavicencio (Eds.), The 60th Annual Meeting of the Association for Computational Linguistics: ACL 2022 : proceedings of the conference : May 22-27, 2022 (Vol. 1, pp. 8446-8459). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2204.10432, https://doi.org/10.18653/v1/2022.acl-long.578[details]
Pradeep, R., Liu, Y., Zhang, X., Li, Y., Yates, A., & Lin, J. (2022). Squeezing Water from a Stone: A Bag of Tricks for Further Improving Cross-Encoder Effectiveness for Reranking. In M. Hagen, S. Verberne, C. Macdonald, C. Seifert, K. Balog, K. Nørvåg, & V. Setty (Eds.), Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022 : proceedings (Vol. I, pp. 655–670). (Lecture Notes in Computer Science; Vol. 13185). Springer. https://doi.org/10.1007/978-3-030-99736-6_44[details]
Tran, H. D., & Yates, A. (2022). Dense Retrieval with Entity Views. In CIKM '22: proceedings of the 31st ACM International Conference on Information & Knowledge Management : October 17-21, 2022, Atlanta, GA, USA (pp. 1955–1964). The Association for Computing Machinery. https://doi.org/10.1145/3511808.3557285[details]
Jose, K. M., Nguyen, T., MacAvaney, S., Dalton, J., & Yates, A. (2021). DiffIR: Exploring Differences in Ranking Models' Behavior. In SIGIR '21: proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 11-15, 2021, virtual event, Canada (pp. 2595-2599). Association for Computing Machinery. https://doi.org/10.1145/3404835.3462784[details]
MacAvaney, S., Yates, A., Feldman, S., Downey, D., Cohan, A., & Goharian, N. (2021). Simplified Data Wrangling with ir_datasets. In SIGIR '21: proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 11-15, 2021, virtual event, Canada (pp. 2429-2436). Association for Computing Machinery. https://doi.org/10.48550/arXiv.2103.02280, https://doi.org/10.1145/3404835.3463254[details]
Mackie, I., Dalton, J., & Yates, A. (2021). How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset. In SIGIR '21: proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 11-15, 2021, virtual event, Canada (pp. 2335–2341). Association for Computing Machinery. https://doi.org/10.1145/3404835.3463262[details]
Tigunova, A., Mirza, P., Yates, A., & Weikum, G. (2021). PRIDE: Predicting Relationships in Conversations. In M-C. Moens, X. Huang, L. Specia, & S. W. Yih (Eds.), 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 4636–4650). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.380[details]
Zheng, Z., Hui, K., He, B., Han, X., Sun, L., & Yates, A. (2021). Contextualized query expansion via unsupervised chunk selection for text retrieval. Information Processing & Management, 58(5), Article 102672. https://doi.org/10.1016/j.ipm.2021.102672[details]
2023
Bénédict, G., Zhang, R., Metzler, D., Yates, A., Deffayet, R., Hager, P., & Jullien, S. (2023). Report on the 1st Workshop on Generative Information Retrieval (Gen-IR 2023) at SIGIR 2023. SIGIR Forum, 57(2), Article 13. https://doi.org/10.1145/3642979.3642995[details]
Razniewski, S., Yates, A. C., Kassner, N., & Weikum, G. (2021). Language Models As or For Knowledge Bases. Paper presented at 4th Workshop on Deep Learning for Knowledge Graphs, DL4KG 2021, Virtual, Online. https://doi.org/10.48550/arXiv.2110.04888
2024
Bleeker, M. J. R. (2024). Multi-modal learning algorithms for sequence modeling and representation learning. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
Fang, Y. (2023). Machine learning tasks and representations for heterogeneous information networks. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
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