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Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings., , , , , , и . TextGraphs@NAACL-HLT, стр. 38-48. Association for Computational Linguistics, (2018)To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders., , и . WSDM, стр. 67-76. ACM, (2024)Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema., , , , , , , , , и . TAC, NIST, (2016)Superpixel-based large displacement optical flow., и . ICIP, стр. 3835-3839. IEEE, (2013)Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection., , , и . NAACL-HLT, стр. 485-495. Association for Computational Linguistics, (2018)Using error decay prediction to overcome practical issues of deep active learning for named entity recognition., , , , и . Mach. Learn., 109 (9-10): 1749-1778 (2020)Open Aspect Target Sentiment Classification with Natural Language Prompts., , , , , и . EMNLP (1), стр. 6311-6322. Association for Computational Linguistics, (2021)Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond, , , , и . Findings of the Association for Computational Linguistics: ACL 2023, стр. 12707--12730. Toronto, Canada, Association for Computational Linguistics, (июля 2023)To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders., , и . CoRR, (2023)Modeling Exercise Relationships in E-Learning: A Unified Approach., , и . EDM, стр. 532-535. International Educational Data Mining Society (IEDMS), (2015)