Lexical Semantic Change (LSC) is the task of identifying, interpreting, and assessing the
possible change over time in the meanings of a target word. Traditionally, LSC has been …

Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change
(LSC). Current evaluations typically focus on a specific task known as Graded Change …

Detecting temporal semantic changes of words is an important task for various NLP
applications that must make time-sensitive predictions. Lexical Semantic Change Detection …

This paper describes the organization and findings of AXOLOTL'24, the first multilingual
explainable semantic change modeling shared task. We present new sense-annotated …

Modeling lexical semantics is a challenging task, often suffering from interpretability pitfalls.
In this paper, we delve into the generation of dictionary-like sense definitions and explore …

Objectives To develop an automated pipeline for extracting prostate cancer-related
information from clinical notes. Materials and methods This retrospective study included …

The advent of large language models (LLMs) has significantly improved performance across
various Natural Language Processing tasks. However, the performance of LLMs has been …

Abstract Lexical Semantic Change Detection (LSCD) aims to detect language change from a
diachronic corpus over time. We can see that over the last two decades there has been a …

Background: The introduction of benchtop NMR instruments has made NMR spectroscopy a
more accessible, affordable option for research and industry, but the lower spectral …

This paper investigates edge induction as a method for augmenting Word Usage Graphs, in
which word usages (nodes) are connected through scores (edges) representing semantic …