# Diachronic Word Embeddings

11 papers with code • 0 benchmarks • 0 datasets

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# Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change

Understanding how words change their meanings over time is key to models of language and cultural evolution, but historical data on meaning is scarce, making theories hard to develop and test.

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# Temporal Word Analogies: Identifying Lexical Replacement with Diachronic Word Embeddings

One well-known property of word embeddings is that they are able to effectively model traditional word analogies ({}word $w_1$ is to word $w_2$ as word $w_3$ is to word $w_4${''}) through vector addition.

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# Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914

We investigate some aspects of the history of antisemitism in France, one of the cradles of modern antisemitism, using diachronic word embeddings.

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# Training Temporal Word Embeddings with a Compass

5 Jun 2019

Temporal word embeddings have been proposed to support the analysis of word meaning shifts during time and to study the evolution of languages.

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# Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models

Language usage can change across periods of time, but document classifiers models are usually trained and tested on corpora spanning multiple years without considering temporal variations.

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# Contextualized Diachronic Word Representations

We devise a novel attentional model, based on Bernoulli word embeddings, that are conditioned on contextual extra-linguistic (social) features such as network, spatial and socio-economic variables, which are associated with Twitter users, as well as topic-based features.

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9 Sep 2019

However, simply knowing that a word has changed in meaning is insufficient to identify the instances of word usage that convey the historical or the newer meaning.

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# Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers

12 Mar 2021

This paper supplements recent qualitative work on the role of women in abolition's vanguard, as well as the role of the Black press, with a quantitative text modeling approach.

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# Words with Consistent Diachronic Usage Patterns are Learned Earlier: A Computational Analysis Using Temporally Aligned Word Embeddings

In this study, we use temporally aligned word embeddings and a large diachronic corpus of English to quantify language change in a data-driven, scalable way, which is grounded in language use.

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# DUKweb: Diachronic word representations from the UK Web Archive corpus

2 Jul 2021

Lexical semantic change (detecting shifts in the meaning and usage of words) is an important task for social and cultural studies as well as for Natural Language Processing applications.

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