Kato, M. P., Liu, Y., Kando, N., & Clarke, C. (2021). Report on the 15th Round of NII Testbeds and Community for Information Access Research (NTCIR-15) SIGIR Forum, 55, 1-21. https://doi.org/10.1145/3527546.3527570
References
Filter by:
Xue, H., Salim, F. D., Ren, Y., & Clarke, C. (2021). Translating Human Mobility Forecasting Through Natural Language Generation ArXiv, abs/2112.11481. Retrieved from https://arxiv.org/abs/2112.11481
Brown, D. G., Byl, L., & Grossman, M. (2021). Are Machine Learning Corpora "Fair Dealing" Under Canadian Law? Presented at the Are Machine Learning Corpora "Fair Dealing" Under Canadian Law? conference. Retrieved from https://computationalcreativity.net/iccc21/wp-content/uploads/2021/09/ICCC_2021_paper_68.pdf
Arabzadeh, N., Yan, X., & Clarke, C. (2021). Predicting Efficiency/Effectiveness Trade-Offs for Dense vs. Sparse Retrieval Strategy Selection ArXiv, abs/2109.10739. Retrieved from https://arxiv.org/abs/2109.10739
Toman, D., & Wedell, G. (2021). Projective Beth Definability and Craig Interpolation for Relational Query Optimization (Material to Accompany Invited Talk) Presented at the Projective Beth Definability and Craig Interpolation for Relational Query Optimization (Material to Accompany Invited Talk) conference. Retrieved from http://ceur-ws.org/Vol-3009/invited1.pdf
Lin, J. (2021). A Proposed Conceptual Framework for a Representational Approach To Information Retrieval SIGIR Forum, 55, 1-4. https://doi.org/10.1145/3527546.3527552
Near, J. P., & He, X. (2021). Differential Privacy for Databases Foundations and Trends in Databases, 11, 109-225. https://doi.org/10.1561/1900000066
Yang, J.-H., Ma, X., & Lin, J. (2021). Sparsifying Sparse Representations for Passage Retrieval by Top-K Masking ArXiv, abs/2112.09628. Retrieved from https://arxiv.org/abs/2112.09628
Sheshbolouki, A., & Ozsu, T. (2021). Scale-Invariant Strength Assortativity of Streaming Butterflies ArXiv, abs/2111.12217. Retrieved from https://arxiv.org/abs/2111.12217
Li, M., Li, M., Xiong, K., & Lin, J. (2021). Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering Presented at the Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering conference. Retrieved from https://aclanthology.org/2021.findings-emnlp.26