Annotated data plays a critical role in Natural Language Processing (NLP) in training
models and evaluating their performance. Given recent developments in Large Language …

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

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 …

In the universe of Natural Language Processing, Transformer-based language models like
BERT and (Chat) GPT have emerged as lexical superheroes with great power to solve open …

This paper introduces a large collection of time series data derived from Twitter,
postprocessed using word embedding techniques, as well as specialized fine-tuned …

Despite its relevance, the maturity of NLP for social media pales in comparison with general-
purpose models, metrics and benchmarks. This fragmented landscape makes it hard for the …

Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually
operationalized based on two subsequently applied usage-level tasks: First, Word-in …

The WiC-ITA shared task aims to determine whether a word appearing in two distinct
sentences carries the same meaning. The task consists of two subtasks: binary classification …

Due to the complexity of the biomedical domain, the ability to capture semantically
meaningful representations of terms in context is a long-standing challenge. Despite …