Estimating the semantic similarity between text data is one of the challenging and open
research problems in the field of Natural Language Processing (NLP). The versatility of …

Polysemy is the type of lexical ambiguity where a word has multiple distinct but related
interpretations. In the past decade, it has been the subject of a great many studies across …

Standard digital communication techniques allow us to set aside the meaning of the
messages to concentrate on the transmission of bits efficiently and reliably. However, with …

In this study, we tackle the task of automatically discerning the level of semantic relatedness
between pairs of sentences. Specifically, Task 1 at SemEval-2024 involves predicting the …

Most words are ambiguous--ie, they convey distinct meanings in different contexts--and
even the meanings of unambiguous words are context-dependent. Both phenomena …

The evaluation of semantic relatedness between web resources is still an open challenge.
This paper focuses on knowledge-based methods that provide an alternative to corpus …

One of the central aspects of contextualised language models is that they should be able to
distinguish the meaning of lexically ambiguous words by their contexts. In this paper we …

This study carries out a systematic intrinsic evaluation of the semantic representations
learned by state-of-the-art pre-trained multimodal Transformers. These representations are …

Semantic Textual Relatedness holds significant relevance in Natural Language Processing,
finding applications across various domains. Traditionally, approaches to STR have relied …

The utilization of word embeddings—powerful models computed through Neural Network
architectures that encode words as vectors—has witnessed rapid growth across various …