Embedding Techniques

A range of language modeling and feature learning methods in Natural Language Processing (NLP), where words or phrases are mapped to vectors of real numbers. These techniques allow semantically similar words to achieve comparable vector representations, facilitating tasks like semantic analysis and context recognition. Core to these techniques are embedding algorithms which learn these vector representations from large text corpora. By analyzing the distribution and usage patterns of words within a vast array of texts, these algorithms encode linguistic items into dense vectors, capturing nuanced semantic relationships and enabling sophisticated language understanding and processing capabilities.