| Challenge: | Diachronic distributional models track changes in word use over time using a continuous variable and a synthetic task to measure the semantic trajectory of a word. |
| Approach: | They propose a deep neural network diachronic distributional model that represents time as a continuous variable and model a word’s usage as . a synthetic task which measures how well a model captures the semantic trajectory of a . word over time. |
| Outcome: | The proposed model can capture the semantic trajectory of a word over time and can measure the speed of lexical change. |
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Analysing Lexical Semantic Change with Contextualised Word Representations (2020.acl-main)
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| Challenge: | Existing studies on lexical semantic change have focused on detecting and characterising word meaning shifts using distributional semantic models. |
| Approach: | They propose a method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change along time with three proposed metrics. |
| Outcome: | The proposed method captures a variety of synchronic and diachronic linguistic phenomena and is highly reproducible and reproducible. |
Diachronic word embeddings and semantic shifts: a survey (C18-1)
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| Challenge: | Existing methods for tracing time-related semantic shifts with word embedding models lack the cohesion, common terminology and shared practices of more established areas of natural language processing. |
| Approach: | They propose several axes along which these methods can be compared and propose a framework for comparison. |
| Outcome: | The proposed methods are compared with existing methods and outline their main challenges and potential applications. |
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (P19-1)
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| Challenge: | Recent studies show that document classifiers can become more stable over time when trained in ways that account for temporal variations. |
| Approach: | They propose a method for embedding diachronic word embedds into document classification models . they propose 'time-driven neural classification model' that accounts for temporal variations . |
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Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View (P19-1)
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| Challenge: | Existing word embeddings only assign one vector to a word for a time period, thus they face the meaning conflation deficiency. |
| Approach: | They propose a sense representation and tracking framework based on deep contextualized embeddings that can be used to answer what and when the word meaning changes. |
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Short-Term Meaning Shift: A Distributional Exploration (N19-1)
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| Challenge: | a new study examines the phenomenon of short-term meaning shift in online communities . the authors use distributional representations to explore the phenomenon . |
| Approach: | They propose to use distributional representations to explore short-term meaning shift in online communities. |
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Sequential Modelling of the Evolution of Word Representations for Semantic Change Detection (2020.emnlp-main)
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| Challenge: | Existing models that detect semantically shifted words do not account for its evolution through time. |
| Approach: | They propose three variants of sequential models for detecting semantically shifted words . they demonstrate that temporal modelling of word representations yields a clear-cut advantage . |
| Outcome: | The proposed models account for the changes in word representations over time. |
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains (P19-1)
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| Challenge: | Existing models for diachronic and synchronic detection of lexical semantic divergences are superficial and lack of comparison. |
| Approach: | They propose to extend benchmark models on a common state-of-the-art evaluation task . they also demonstrate that the same evaluation task and modelling approaches can be utilised for synchronic detection of domain-specific sense divergences in the field of term extraction. |
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Diachronic degradation of language models: Insights from social media (P18-2)
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| Challenge: | Existing studies have explored whether and how language models degrade over time, i.e. why they fail to work on contemporary language. |
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Exploring Word Usage Change with Continuously Evolving Embeddings (2021.acl-demo)
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| Challenge: | a new method to track word usage changes is proposed for text datasets that are collected over a longer period of time. |
| Approach: | They propose a way to track word usage changes via continuously evolving embeddings . they demonstrate an interactive web app that can explore semantic shifts with interactive plots a text . |
| Outcome: | The proposed method can be used to analyze word usage changes with interactive plots. |
Infinite SCAN: An Infinite Model of Diachronic Semantic Change (2022.emnlp-main)
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| Challenge: | Existing methods for capturing semantic changes using word embeddings cannot account for existence of each sense and its relative importance. |
| Approach: | They propose a Bayesian model that can estimate the number of senses of words and their changes through time using a dynamic topic model and a logistic stick-breaking process. |
| Outcome: | The proposed model outperforms the baseline model and investigates the semantic changes of several well-known target words using the CCOHA corpus. |