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.

Similar Papers

The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change (2024.eacl-demo)

Copied to clipboard

Challenge: DURel is an open source tool for semantic proximity between word uses.
Approach: They present an open-source tool for the annotation of semantic proximity between word uses.
Outcome: The proposed tool supports standardized human annotation and computational annotation, building on recent advances with Word-in-Context models.
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.
TRoTR: A Framework for Evaluating the Re-contextualization of Text Reuse (2024.emnlp-main)

Copied to clipboard

Challenge: Existing methods for detecting text reuse focus on recontextualization . current approaches focus on text reuse across a diachronic corpus .
Approach: They propose a framework that relies on topic relatedness for evaluating the diachronic change of context in which text is reused.
Outcome: The proposed framework evaluates biblical text reuse human-annotated with topic relatedness . it exhibits greater sensitivity to textual similarity than topic relatedity, the authors show .
Multi-word Measures: Modeling Semantic Change in Compound Nouns (2025.findings-acl)

Copied to clipboard

Challenge: Compound words provide a multifaceted challenge for diachronic models of semantic change . novel sense-targeting approach targets both noun compounds and their constituent parts .
Approach: They propose a dataset of relatedness judgements of noun compounds in English and german . they use contrasting vector representations to evaluate their ability to cluster example sentence pairs .
Outcome: The proposed approach captures diachronic meaning changes for multi-word expressions without condensing individual senses into an aggregate value.
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.
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.
DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages (2021.emnlp-main)

Copied to clipboard

Challenge: Existing methods for graded contextual word meaning annotation have not been implemented yet.
Approach: They propose a multi-round incremental annotation process and a clustering algorithm to group usages into senses to create a large-scale dataset.
Outcome: The proposed method is the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based on 100,000 human semantic proximity judgments.
What Can Diachronic Contexts and Topics Tell Us about the Present-Day Compositionality of English Noun Compounds? (2024.lrec-main)

Copied to clipboard

Challenge: Existing methods to determine the semantic relatedness between compounds and constituents have applied a synchronic perspective, but this study examines what diachronic changes in contexts and semantic topics reveal about the compounds’ present-day compositionality.
Approach: They propose to use two diachronic vector spaces to model compositional patterns between compounds with low and high present-day compositionality.
Outcome: The proposed model performs on par with co-occurrence space and captures similar information.
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.
NorDiaChange: Diachronic Semantic Change Dataset for Norwegian (2022.lrec-1)

Copied to clipboard

Challenge: NorDiaChange is the first dataset of diachronic semantic change on the lexical level for Norwegian.
Approach: They describe a manual annotation process for a new dataset of diachronic semantic change for Norwegian.
Outcome: The proposed dataset covers the time periods related to pre- and post-war events, oil and gas discovery in Norway, and technological developments.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations