Doc2vec
Doc2vec (Document to Vector), also known as Paragraph Vector, is a natural language processing technique that learns fixed-length vector representations for variable-length text documents, such as sentences, paragraphs, or entire articles. It extends the Word2vec model by adding a paragraph ID to predict words in a context, enabling semantic similarity comparisons and clustering of documents. This method is widely used in tasks like document classification, sentiment analysis, and information retrieval.
Developers should learn Doc2vec when working on projects that require understanding or comparing the semantic content of text documents, such as building recommendation systems, document clustering, or automated tagging. It is particularly useful in scenarios where traditional bag-of-words models fail to capture context and meaning, such as in legal document analysis, news article categorization, or customer feedback processing. By learning dense vector representations, it allows for efficient similarity calculations and integration into machine learning pipelines.