Sequential Modelling of the Evolution of Word Representations for Semantic Change Detection (2020.emnlp-main)
Copied to clipboard
| 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. |
Similar Papers
Diachronic word embeddings and semantic shifts: a survey (C18-1)
Copied to clipboard
| 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. |
Computational modeling of semantic change (2024.eacl-tutorials)
Copied to clipboard
| Challenge: | Languages change constantly over time, influenced by social, technological, cultural and political factors that affect how people express themselves. |
| Approach: | They propose to categorise the types of change, the causes and the mechanisms underlying the different types of changes using large diachronic corpora and evaluation benchmarks. |
| Outcome: | In historical linguistics, tools and methods have been developed to analyse the process . they include categorisations of types of change, causes and mechanisms . but traditional methods, while informative, are often based on small, carefully curated samples. |
Analysing Lexical Semantic Change with Contextualised Word Representations (2020.acl-main)
Copied to clipboard
| 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. |
Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings (D19-1)
Copied to clipboard
| 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. |
| Approach: | They propose a new evaluation framework for semantic change detection using whole time series and a Twitter dataset spanning 5.5 years. |
| Outcome: | The proposed framework shows that using whole time series is preferable over continuously trained embeddings for long time periods and that the reference point matters. |
Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View (P19-1)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework is effective in representing fine-grained word senses, and brings a significant improvement in word change detection task. |
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (P19-1)
Copied to clipboard
| 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 . |
| Outcome: | The proposed model can be trained on six corpora and make it more robust over time. |
Substitution-based Semantic Change Detection using Contextual Embeddings (2023.acl-short)
Copied to clipboard
| Challenge: | a simplified approach to measuring semantic change using contextual embeddings is proposed . the static word vectors used for measuring semantic changes are difficult to interpret . |
| Approach: | They propose a simplified approach to measuring semantic change using contextual embeddings . they use the Jensen-Shannon Divergence between the distributions of most probable replacements for masked words in different time periods to measure semantic change. |
| Outcome: | The proposed approach is interpretable, efficient and much more efficient than static embeddings. |
Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types (2024.acl-long)
Copied to clipboard
| Challenge: | Existing models for detecting semantic change in corpora have been disregarded due to lack of knowledge of the nature of semantic change and the way it takes place. |
| Approach: | They propose a model that leverages synchronic lexical relations and definitions of word meanings to detect these types of change. |
| Outcome: | The proposed model can detect changes in a digitized version of Blank's dataset and improve human judgments of semantic relatedness and binary Lexical Semantic Change Detection. |
Definition generation for lexical semantic change detection (2024.findings-acl)
Copied to clipboard
| Challenge: | a number of studies have attempted to bridge the gap between lexical semantic change detection and sense-based LSCD methods. |
| Approach: | They propose a sense distribution based LSCD method which uses contextualized word definitions as 'senses' they argue that the method preserves interpretability and allows to inspect the reasons behind a specific shift in terms of discrete definitions-as-sense. |
| Outcome: | The proposed method outperforms previous sense-based methods on five datasets and three languages and preserves interpretability and allows to inspect the reasons behind a specific shift in terms of discrete definitions-as-senses. |
Swap and Predict – Predicting the Semantic Changes in Words across Corpora by Context Swapping (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Detecting semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions. |
| Approach: | They propose a method that randomly swaps contexts between two different corpora to detect whether a given word changes its meaning . they then use a pretrained masked language model to generate contextualised word embeddings of w, which are then used to predict the semantic changes of words in four languages . |
| Outcome: | The proposed method achieves significant performance improvements compared to baselines for the English semantic change prediction task. |