Word

embedding Word embedding is a method used in the natural language processing field, which maps words in a text to points in a multi-dimensional vector space. The aim of this mapping is to represent the semantic relationships between words and phrases of the text. This allows computers to better understand the meanings of sentences, words, and phrases in a quantitative approach. Word embeddings are used in many natural language processing tasks such as sentiment analysis, machine translation, and text summarization. They can also be used for various tasks such as text classification, topic modelling, and document clustering. This technology enables computers to better understand language, which has a wide range of applications in the fields of artificial intelligence, natural language processing and machine learning.