languageembedding
Language embedding refers to the representation of linguistic units as vectors in a continuous, high-dimensional space. These embeddings encode semantic and syntactic properties and are used as inputs to neural models for natural language processing. The term covers word, subword, sentence, document, and language-level representations. Word embeddings map individual tokens to dense vectors, while contextual embeddings generate dynamic representations depending on surrounding text. Sentence and document embeddings aim to capture overall meaning, often via pooling or specialized architectures. In multilingual settings, language embeddings or joint multilingual embeddings map languages or texts from different languages into a shared space to enable cross-language transfer and comparison.
Common techniques include static embeddings such as Word2Vec, GloVe, and FastText, and contextual models such as
Applications span machine translation, cross-lingual information retrieval, sentiment analysis, named entity recognition, and other NLP tasks,
History notes that word embeddings rose to prominence with Word2Vec and GloVe in the early 2010s, while