| 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. |
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| Challenge: | Existing methods for word embeddings have been used to model semantic relations with word embeds. |
| Approach: | They propose a method that leverages contextual embeddings for diachronic semantic shift detection by generating time specific word representations from BERT embedds. |
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Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices (2025.coling-main)
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| Challenge: | Existing methods to analyze word sense proportions are insufficient for understanding semantic shifts . et al., 2018: semantic shift and its effects. |
| Approach: | They propose a framework for how semantic shifts occur over multiple time periods by using word embeddings. |
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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. |
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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. |
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Detecting Contact-Induced Semantic Shifts: What Can Embedding-Based Methods Do in Practice? (2021.emnlp-main)
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| Challenge: | Existing work on semantic change detection methods has focused on generic research questions and datasets, using them as a training ground for proof-of-concept studies. |
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A Methodology for Building a Diachronic Dataset of Semantic Shifts and its Application to QC-FR-Diac-V1.0, a Free Reference for French (2022.lrec-1)
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| Challenge: | Existing algorithms to detect semantic shifts have been criticized for their difficulty in evaluating them. |
| Approach: | They propose a method for building a reference dataset for semantic shift detection . they use a word-sense disambiguation model to associate a date of first appearance to all senses of a term . |
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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. |
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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. |
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Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings (D19-1)
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| Challenge: | Word embeddings are increasingly used for automatic detection of semantic change, but a robust evaluation and systematic comparison of the choices involved has been lacking. |
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Analyzing the Surprising Variability in Word Embedding Stability Across Languages (2021.emnlp-main)
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| Challenge: | Word embeddings are powerful representations that form the foundation of many natural language processing architectures. |
| Approach: | They explore word embedding stability in a wide range of languages to gain insight into their stability. |
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