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An expectation-maximization algorithm for query translation based on pseudo-relevant documents.

, , , and . Inf. Process. Manag., 53 (2): 371-387 (2017)

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Pseudo-Relevance Feedback Based on Matrix Factorization., , , and . CIKM, page 1483-1492. ACM, (2016)Building a multi-domain comparable corpus using a learning to rank method., , , , , and . Nat. Lang. Eng., 22 (4): 627-653 (2016)Unsupervised Formation Matching in Highly Inflected Languages., , , and . CoRR, (2016)Dimension Projection Among Languages Based on Pseudo-Relevant Documents for Query Translation., , , , and . ECIR, volume 10193 of Lecture Notes in Computer Science, page 493-499. (2017)Data-Driven Mapping Between Functional Connectomes Using Optimal Transport., , and . MICCAI (3), volume 12903 of Lecture Notes in Computer Science, page 293-302. Springer, (2021)A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences., , , , and . CNI@MICCAI, volume 11848 of Lecture Notes in Computer Science, page 64-73. Springer, (2019)Revisiting Optimal Rank Aggregation: A Dynamic Programming Approach., , , , and . ICTIR, page 353-356. ACM, (2015)Enhancement attacks in biomedical machine learning., , and . CoRR, (2023)Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries., , and . COLING, page 1725-1733. ACL, (2016)Stacking multiple optimal transport policies to map functional connectomes., , , and . CISS, page 1-6. IEEE, (2023)