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 neural networks to learn word embeddings from large text corpora. It employs two main architectures: Continuous Bag of Words (CBOW) and Skip-gram. CBOW predicts a target word from its context, while Skip-gram predicts context words from a target word.

Another widely used method is GloVe (Global Vectors for Word Representation), developed by Stanford University. GloVe leverages global word-word co-occurrence statistics from a corpus to create word embeddings. It aims to capture both local and global statistical information, resulting in embeddings that are both accurate and efficient.

FastText, developed by Facebook's AI Research lab, extends Word2Vec by representing words as bags of character n-grams. This approach helps capture subword information, making FastText particularly effective for morphologically rich languages and out-of-vocabulary words.

Contextual embeddings, such as ELMo (Embeddings from Language Models) and BERT (Bidirectional Encoder Representations from Transformers), go beyond static embeddings by considering the context in which words appear. ELMo uses a bidirectional LSTM to generate word embeddings that are context-dependent. BERT, on the other hand, employs a transformer architecture to produce contextual embeddings that capture bidirectional context.

Each embedding method has its strengths and weaknesses, and the choice of method depends on the specific task and dataset at hand. However, embedding methods have significantly advanced the field of natural language processing, enabling more accurate and efficient language understanding and generation.