Szóbeágyazás

Szóbeágyazás, also known as word embedding, is a technique in natural language processing (NLP) where words are represented as dense, low-dimensional vectors in a continuous vector space. Unlike traditional sparse representations like one-hot encoding, which can be very high-dimensional and don't capture semantic relationships, word embeddings place words with similar meanings closer to each other in the vector space.

The core idea behind szóbeágyazás is to learn these vector representations from large amounts of text data. Algorithms like Word2Vec, GloVe, and FastText are commonly used for this purpose. These algorithms analyze the context in which words appear. Words that frequently appear in similar contexts are likely to have similar meanings and thus will be mapped to nearby vectors. For example, the vectors for "king" and "queen" might be close, and the vector difference between "king" and "man" might be similar to the difference between "queen" and "woman."

The resulting word vectors can be used as input features for various NLP tasks. These include sentiment analysis, machine translation, text classification, and question answering. By capturing semantic and syntactic relationships between words, szóbeágyazás significantly improves the performance of these downstream applications compared to simpler word representation methods. The ability to represent words as numerical vectors that capture meaning is a fundamental advancement in modern NLP.