Szóvektoros

Szóvektoros refers to a concept in natural language processing and computational linguistics related to the representation of words as numerical vectors. This technique, often called word embedding, aims to capture semantic relationships between words. Words with similar meanings or contexts are expected to have vectors that are close to each other in a multi-dimensional space. These vectors are typically learned from large text corpora using various algorithms such as Word2Vec, GloVe, or FastText. The process involves analyzing the co-occurrence patterns of words within a given text. For example, words like "king" and "queen" might appear in similar contexts and thus have similar vector representations, potentially with a predictable mathematical relationship to "man" and "woman" vectors. Szóvektoros representations are fundamental to many downstream NLP tasks, including machine translation, sentiment analysis, text classification, and question answering systems, enabling computers to process and understand human language in a more nuanced way.