Advantages of Doc2Vec
- Doc2Vec can capture the semantic meaning of entire documents or paragraphs, unlike traditional bag-of-words models that treat each word independently.
- It can be used to generate document embeddings, which can be used for a variety of downstream tasks such as document classification, clustering, and similarity search.
- Doc2Vec can handle unseen words by leveraging the context in which they appear in the document corpus, unlike methods such as TF-IDF that rely on word frequency in the corpus.
- It can be trained on large corpora using parallel processing, making it scalable to big data applications.
- It is flexible and can be easily customized by adjusting various hyperparameters such as the dimensionality of the document embeddings, the number of training epochs, and the training algorithm.
Doc2Vec in NLP
Doc2Vec is also called a Paragraph Vector a popular technique in Natural Language Processing that enables the representation of documents as vectors. This technique was introduced as an extension to Word2Vec, which is an approach to represent words as numerical vectors. While Word2Vec is used to learn word embeddings, Doc2Vec is used to learn document embeddings. In this article, we will discuss the Doc2Vec approach in detail.