RuSemShift: a dataset of historical lexical semantic change in Russian (2020.coling-main)
Copied to clipboard
| Challenge: | et al. (2017) lexical semantic change analysis is still mostly done for English because of limited resources. |
| Approach: | They present a large-scale manually annotated test set for semantic change modeling in Russian . they use DURel framework to annotate Russian for two long-term time periods . |
| Outcome: | The proposed model performs well, but there are still areas for improvement . the results are promising, but the authors say they need to improve . |
Similar Papers
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. |
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. |
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. |
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. |
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian (C18-1)
Copied to clipboard
| Challenge: | RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss’ kappa of 0.58 (3 annotations per post). |
| Approach: | They propose to use RuSentiment to annotate social media posts in Russian with a kappa of 0.58 and a set of annotation guidelines that are extensible to other languages. |
| Outcome: | The proposed dataset is the largest in its class for Russian, with 31,185 posts annotated with Fleiss’ kappa of 0.58 (3 annotations per post). |
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. |
Measuring and Modeling Language Change (N19-5)
Copied to clipboard
| Challenge: | This tutorial will help researchers answer questions fundamental to the social sciences and humanities . |
| Approach: | This tutorial is designed to help researchers answer questions in the social sciences and humanities . it synthesizes recent computational techniques for handling and modeling temporal data . |
| Outcome: | The tutorial will synthesize recent techniques for handling and modeling temporal data, such as dynamic word embeddings, and identify useful tools for social scientists and digital humanities scholars. |
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%. |
Constructing a Lexical Resource of Russian Derivational Morphology (2022.lrec-1)
Copied to clipboard
| Challenge: | In Natural Language Processing of Russian, the inflection is satisfactorily processed, but there are only a few machine-trackable resources that capture derivations . |
| Approach: | They propose to use machine-learning methods to improve Russian inflection and derivational resources by using a database of more than 300 thousand lexemes and 164 thousand binary derivations. |
| Outcome: | The proposed method includes more than 300 thousand lexemes connected with more than 164 thousand binary derivational relations. |
Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change (N18-2)
Copied to clipboard
| Challenge: | Existing frameworks for evaluating lexical semantic change are limited . evaluation of lexicals is a major obstacle in the field of semantic change detection . |
| Approach: | They propose a framework that extends synchronic polysemy annotation to diachronic changes in lexical meaning to counteract lack of resources for evaluating computational models of lexiconal semantic change. |
| Outcome: | The proposed framework exploits an intuitive notion of semantic relatedness and distinguishes between innovative and reductive meaning changes with high inter-annotator agreement. |