Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change
By Haim Dubossarsky, Simon Hengchen, Nina Tahmasebi, Dominik Schlechtweg. 2019
108 citations recorded by the source corpus.
State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.
Subfields: Artificial Intelligence, Cultural Studies, Natural Language Processing, Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- William W. Cohen
- Bhuwan Dhingra
- Jacob Eisenstein
- Julian Martin Eisenschlos
- D. Gillick
- Mohit Bansal
- Kyunghyun Cho
- Ruslan Salakhutdinov
In Cultural Studies
In Natural Language Processing
- William W. Cohen
- Bhuwan Dhingra
- Jacob Eisenstein
- Julian Martin Eisenschlos
- D. Gillick
- Mohit Bansal
- Kyunghyun Cho
- Ruslan Salakhutdinov
In Representation & Self-Supervised Learning
Citations in the corpus
- Time-Aware Language Models as Temporal Knowledge Bases (2021, background): “Particularly related is the approach of Dubossarsky et al. (2019), who learn time-sensitive embeddings by concatenating each word token with the decade in which it appears.”
- A Model of the Language Process (2026, unknown): “Benchmarking the semantic change detection performance of a model is challenging due to the lack of strong standardized datasets for the task (Kutuzov et al., 2018; Dubossarsky et al., 2019; Schlechtweg et al., 2020).”