| Challenge: | Existing approaches to Lexical Semantic Change Detection are limited. |
| Approach: | They propose a shift from change detection to change discovery by fine-tuning a type-based and a token-based approach on recently published German data. |
| Outcome: | The proposed models can be applied to discover new words undergoing meaning change from the full corpus vocabulary. |
Similar Papers
Current Semantic-change Quantification Methods Struggle with Semantic Change Discovery in the Wild (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for lexical semantic-change detection quantify changes in the meaning of words over time. |
| Approach: | They propose to use a top-k setup to evaluate semantic-change discovery despite lacking complete annotations on a battery of semantic-changing detection methods. |
| Outcome: | The proposed setup extends the annotations in the commonly used LiverpoolFC and SemEval-EN benchmarks by 85% and 90%. |
Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection (2021.eacl-main)
Copied to clipboard
| Challenge: | Lexical semantic change detection is a new and innovative research field. |
| Approach: | They propose to pre-train on large corpora and refine on diachronic target corpors to improve performance. |
| Outcome: | The proposed models improve on large corpora and diachronic target corpors . the proposed models are compared with existing models in a variety of learning scenarios . |
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. |
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. |
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. |
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains (P19-1)
Copied to clipboard
| 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. |
| Outcome: | The proposed model can be utilised for the detection of domain-specific sense divergences in the field of term extraction. |
Toward Sentiment Aware Semantic Change Analysis (2024.eacl-srw)
Copied to clipboard
| Challenge: | Current approaches to analyze semantic change are lagging behind . current methods only detect semantic change as a binary classification or graded change scores . |
| Approach: | They propose to augment models of semantic change with sentiment information . they demonstrate that existing models extract reliable sentiment information from historical corpora . |
| Outcome: | The proposed approach shows mixed results on the English SemEval of Lexical Semantic Change and its associated historical corpora. |
Analyzing Semantic Change through Lexical Replacements (2024.acl-long)
Copied to clipboard
| Challenge: | Modern language models can contextualize words based on their surrounding contexts, but semantic change can compromise this capability. |
| Approach: | They propose a replacement schema where a target word is replaced with lexical replacements of varying relatedness . they leverage the replacement schema as a basis for a novel interpretable model for semantic change . |
| Outcome: | The proposed model is the first to evaluate LLaMa for semantic change detection . it shows that lexical replacements can detect unexpected contexts . |
Can Word Sense Distribution Detect Semantic Changes of Words? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to detect semantic variations of words are not accurate for time-sensitive predictions. |
| Approach: | They propose to use pretrained static sense embeddings to annotate a word's occurrence with a sense id to compare its distributions. |
| Outcome: | The proposed method compares word sense distributions across two corpora to predict meaning change . the results show that pretrained LLMs can detect changes in words over time . |
Explaining and Improving BERT Performance on Lexical Semantic Change Detection (2021.eacl-srw)
Copied to clipboard
| Challenge: | Lexical semantic change detection is still a challenging field due to the success of type-based embeddings in SemEval-2020 Task 1 and other NLP tasks. |
| Approach: | They compare the performance of BERT embeddings with results from the word sense disambiguation dataset underlying SemEval-2020 Task 1 and the Italian follow-up task DIACR-Ita. |
| Outcome: | The proposed model outperforms token-based embeddings on lexical semantic change detection tasks. |