Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019
DOI: 10.18653/v1/p19-1044
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Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change

Abstract: 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 simul… Show more

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Cited by 83 publications
(120 citation statements)
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“…Properties inherent to language can make a difference, such as the aforementioned re-use of archaic variants from the written record (Section 3), or meaning change, which may reasonably resolve competition between variants as they go on to inhabit different niches (automatic methods exist to detect the latter, cf. Dubossarsky et al 2019). This relates to the issue of determining what variants do and which do not actually compete with one other for the same meaning or function, often referred to in sociolinguistics as the problem of the envelope of variation (cf.…”
Section: Limitations For Linguistic Selection Testingmentioning
confidence: 99%
“…Properties inherent to language can make a difference, such as the aforementioned re-use of archaic variants from the written record (Section 3), or meaning change, which may reasonably resolve competition between variants as they go on to inhabit different niches (automatic methods exist to detect the latter, cf. Dubossarsky et al 2019). This relates to the issue of determining what variants do and which do not actually compete with one other for the same meaning or function, often referred to in sociolinguistics as the problem of the envelope of variation (cf.…”
Section: Limitations For Linguistic Selection Testingmentioning
confidence: 99%
“…The data used is the same as described in the corresponding section, but with one major adjustment, which is due to my choice of research design appropriate for detecting the change in word meaning and use. Dubossarsky et al (2019) recently demonstrated that the Temporal Referencing technique has significant advantages over other approaches of detecting genuine semantic change. The idea of the method is, first, to focus on a limited set of words whose change is going to be studied.…”
Section: Resultsmentioning
confidence: 99%
“…Lexical semantic change has been studied with word embeddings [8]. The authors suggest that word embeddings should not be aligned when used to study semantic change, as such an alignment introduces noise.…”
Section: Related Workmentioning
confidence: 99%