Embedding: meaning, definitions and examples
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embedding
[ ɪmˈbɛdɪŋ ]
data representation
Embedding refers to a method of representing data in a lower-dimensional space, typically used in machine learning and natural language processing. It allows for the transformation of complex data into a vector format that can be easily processed by algorithms. Common forms of embedding include word embeddings such as Word2Vec or GloVe, which capture semantic meanings of words. These representations are essential for tasks like sentiment analysis, translation, and information retrieval.
Synonyms
mapping, representation, vectorization.
Examples of usage
- Word embeddings help improve the accuracy of NLP tasks.
- The model uses image embeddings to classify photos.
- Using embeddings, the algorithm can understand similarities between words.
Translations
Translations of the word "embedding" in other languages:
🇵🇹 incorporação
🇮🇳 एम्बेडिंग
🇩🇪 Einbettung
🇮🇩 penyematan
🇺🇦 вбудовування
🇵🇱 osadzanie
🇯🇵 埋め込み
🇫🇷 insertion
🇪🇸 incrustación
🇹🇷 gömme
🇰🇷 임베딩
🇸🇦 تضمين
🇨🇿 vkládání
🇸🇰 vkladanie
🇨🇳 嵌入
🇸🇮 vdelava
🇮🇸 innfellding
🇰🇿 енгізу
🇬🇪 ჩართვა
🇦🇿 gizlətmə
🇲🇽 incrustación
Etymology
The term 'embedding' has its roots in the concept of embedding in mathematics, where it denotes the inclusion of one structure within another. In computer science, particularly in the fields of machine learning and neural networks, the term has been adopted to describe the process of converting high-dimensional data into a low-dimensional vector space. This transformation simplifies the data and helps capture its essential features. The development of word embeddings in the 2010s, especially with algorithms like Word2Vec introduced by Google in 2013, propelled the use of the term into the mainstream of artificial intelligence applications. As machine learning techniques grew more sophisticated, embeddings expanded beyond text, finding applications in images, graphs, and more, significantly impacting data representation across various domains.