Сравнение методов

	Word2Vec	Классификация текстов
Область	Интеллектуальный анализ текста	Интеллектуальный анализ текста
Семейство	Process / pipeline	Process / pipeline
Год появления	2013	—
Автор метода	Tomas Mikolov et al.	—
Тип	Neural word-embedding model	Supervised NLP classification task
Основополагающий источник	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Другие названия	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri	text categorization, document classification, topic classification, metin sınıflandırma
Связанные	4	4
Сводка	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.