Khandel, P., Yates, A., Varbanescu, A. L., Rijke, M. D., & Pimentel, A. D. (2024). Distillation vs. Sampling for Efficient Training of Learning to Rank Models. In H. Oosterhuis, H. Bast, & C. Xiong (Eds.), Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2024, Washington, DC, USA, 13 July 2024 (pp. 51-60). ACM. https://doi.org/10.1145/3664190.3672527
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]
De UvA gebruikt cookies voor het meten, optimaliseren en goed laten functioneren van de website. Ook worden er cookies geplaatst om inhoud van derden te kunnen tonen en voor marketingdoeleinden. Klik op ‘Accepteren’ om akkoord te gaan met het plaatsen van alle cookies. Of kies voor ‘Weigeren’ om alleen functionele en analytische cookies te accepteren. Je kunt je voorkeur op ieder moment wijzigen door op de link ‘Cookie instellingen’ te klikken die je onderaan iedere pagina vindt. Lees ook het UvA Privacy statement.