| SemEval-2020 task 1: Unsupervised lexical semantic change detection D Schlechtweg, B McGillivray, S Hengchen, H Dubossarsky, ... arXiv preprint arXiv:2007.11464, 2020 | 268 | 2020 |
| Outta control: Laws of semantic change and inherent biases in word representation models H Dubossarsky, D Weinshall, E Grossman Proceedings of the 2017 conference on empirical methods in natural language …, 2017 | 195 | 2017 |
| Time-out: Temporal referencing for robust modeling of lexical semantic change H Dubossarsky, S Hengchen, N Tahmasebi, D Schlechtweg arXiv preprint arXiv:1906.01688, 2019 | 134 | 2019 |
| Quantifying the structure of free association networks across the life span. H Dubossarsky, S De Deyne, TT Hills Developmental psychology 53 (8), 1560, 2017 | 124 | 2017 |
| A bottom up approach to category mapping and meaning change. H Dubossarsky, Y Tsvetkov, C Dyer, E Grossman NetWordS, 66-70, 2015 | 78 | 2015 |
| Avoiding the hypothesis-only bias in natural language inference via ensemble adversarial training J Stacey, P Minervini, H Dubossarsky, S Riedel, T Rocktäschel arXiv preprint arXiv:2004.07790, 2020 | 51 | 2020 |
| Verbs change more than nouns: A bottom-up computational approach to semantic change H Dubossarsky, D Weinshall, E Grossman Lingue e linguaggio 15 (1), 7-28, 2016 | 50 | 2016 |
| DWUG: A large resource of diachronic word usage graphs in four languages D Schlechtweg, N Tahmasebi, S Hengchen, H Dubossarsky, ... arXiv preprint arXiv:2104.08540, 2021 | 47 | 2021 |
| The human brain reactivates context-specific past information at event boundaries of naturalistic experiences A Hahamy, H Dubossarsky, TEJ Behrens Nature neuroscience 26 (6), 1080-1089, 2023 | 36 | 2023 |
| Challenges for computational lexical semantic change S Hengchen, N Tahmasebi, D Schlechtweg, H Dubossarsky Computational approaches to semantic change 6, 341, 2021 | 35 | 2021 |
| The secret is in the spectra: Predicting cross-lingual task performance with spectral similarity measures H Dubossarsky, I Vulić, R Reichart, A Korhonen arXiv preprint arXiv:2001.11136, 2020 | 26 | 2020 |
| Coming to your senses: on controls and evaluation sets in polysemy research H Dubossarsky, E Grossman, D Weinshall Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 22 | 2018 |
| Logical reasoning with span-level predictions for interpretable and robust NLI models J Stacey, P Minervini, H Dubossarsky, M Rei arXiv preprint arXiv:2205.11432, 2022 | 12 | 2022 |
| Computational modeling of semantic change N Tahmasebi, H Dubossarsky arXiv preprint arXiv:2304.06337, 2023 | 10 | 2023 |
| Semantic change at large: A computational approach for semantic change research H Dubossarsky Ph. D. thesis, Hebrew University of Jerusalem, Edmond and Lily Safra Center …, 2018 | 10 | 2018 |
| (Chat) GPT v BERT: Dawn of Justice for Semantic Change Detection F Periti, H Dubossarsky, N Tahmasebi arXiv preprint arXiv:2401.14040, 2024 | 8 | 2024 |
| Analyzing Semantic Change through Lexical Replacements F Periti, P Cassotti, H Dubossarsky, N Tahmasebi arXiv preprint arXiv:2404.18570, 2024 | 6 | 2024 |
| Logical reasoning for natural language inference using generated facts as atoms J Stacey, P Minervini, H Dubossarsky, OM Camburu, M Rei arXiv preprint arXiv:2305.13214, 2023 | 6 | 2023 |
| The finer they get: Combining fine-tuned models for better semantic change detection W Zhou, N Tahmasebi, H Dubossarsky Proceedings of the 24th Nordic Conference on Computational Linguistics …, 2023 | 4 | 2023 |
| The Time-Embedding Travelers at WiC-ITA. F Periti, H Dubossarsky EVALITA, 2023 | 4 | 2023 |
