Haim Dubossarsky
Haim Dubossarsky
Research Fellow, University of Cambridge
Verified email at cam.ac.uk
Title
Cited by
Cited by
Year
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
922017
Semeval-2020 task 1: Unsupervised lexical semantic change detection
D Schlechtweg, B McGillivray, S Hengchen, H Dubossarsky, ...
arXiv preprint arXiv:2007.11464, 2020
582020
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
482019
A bottom up approach to category mapping and meaning change.
H Dubossarsky, Y Tsvetkov, C Dyer, E Grossman
NetWordS, 66-70, 2015
422015
Quantifying the structure of free association networks across the life span.
H Dubossarsky, S De Deyne, TT Hills
Developmental psychology 53 (8), 1560, 2017
342017
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
222016
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
152018
The secret is in the spectra: Predicting cross-lingual task performance with spectral similarity measures
H Dubossarsky, I Vulic, R Reichart, A Korhonen
Association for Computational Linguistics, 2020
4*2020
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
42018
Challenges for computational lexical semantic change
S Hengchen, N Tahmasebi, D Schlechtweg, H Dubossarsky
arXiv preprint arXiv:2101.07668, 2021
32021
There is Strength in Numbers: 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
22020
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
J Stacey, P Minervini, H Dubossarsky, S Riedel, T Rocktäschel
EMNLP (1) 2020, 8281-8291, 2020
12020
Proceedings of the Second Workshop on Computational Research in Linguistic Typology
E Vylomova, EM Ponti, E Grossman, AD McCarthy, Y Berzak, ...
Proceedings of the Second Workshop on Computational Research in Linguistic …, 2020
2020
Time for change: Evaluating models of semantic change without evaluation tasks
H Dubossarsky, S Hengchen, N Tahmasebi, D Schlechtweg
Cambridge Language Sciences Annual Symposium 2019: Perspectives on Language …, 2019
2019
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Articles 1–14