The evaluation of the semantic similarity of concepts organized according to taxonomies is a
long-standing problem in computer science and has attracted great attention from …

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

In recent years, word embeddings (WEs) have proven relevant for studying differences and
similarities among job professions and skills required by the labour market across countries …

Semantic similarity is a critical aspect of natural language processing, as it evaluates the
degree of similarity within a knowledge graph. Various computational methods, including …

Semantic similarity is a fundamental task in natural language processing that determines the
similarity between two concepts within a taxonomy. For example, a pair of words (eg, car …

Semantic textual analysis is a natural language processing task that has enjoyed several
research contributions towards solving diverse real-life problems. Vector comparison is a …

The semantic text similarity (STS) estimation between patents is a critical issue for the patent
portfolio analysis. Current methods such as keywords, co-word analysis and even the …

Analyzing the semantic similarity of cross-lingual texts is a crucial part of natural language
processing (NLP). The computation of semantic similarity is essential for a variety of tasks …

Word embeddings have proven extremely useful across many NLP applications in recent
years. Several key linguistic tasks, such as machine translation and transfer learning …

Plagiarism is known as presenting other works as one's own without making proper citation
or giving an explicit acknowledgment. The detection of plagiarism is interesting because it …