Einbettungsmethoden
Einbettungsmethoden, or embedding methods, are techniques used in natural language processing and machine learning to convert discrete data, such as words or sentences, into continuous vector representations. These vector representations, or embeddings, capture semantic meaning and relationships between data points, making them suitable for various downstream tasks like classification, clustering, and translation.
One of the most popular embedding methods is Word2Vec, developed by Google in 2013. Word2Vec uses shallow
Another widely used method is GloVe (Global Vectors for Word Representation), developed by Stanford University. GloVe
FastText, developed by Facebook's AI Research lab, extends Word2Vec by representing words as bags of character
Contextual embeddings, such as ELMo (Embeddings from Language Models) and BERT (Bidirectional Encoder Representations from Transformers),
Each embedding method has its strengths and weaknesses, and the choice of method depends on the specific