Dokumentnivåvektorer

Dokumentnivåvektorer, often shortened to document vectors, are numerical representations of documents in a multi-dimensional space. These vectors are designed to capture the semantic meaning and thematic content of a document. The process of creating document vectors typically involves analyzing the words and phrases within a document and then mapping them to a vector space. Common techniques for generating document vectors include methods like Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and more recently, embedding models such as Word2Vec, Doc2Vec (which is a direct extension of Word2Vec for documents), and Universal Sentence Encoder.

The core idea is that documents with similar meanings or topics will have vectors that are close to each other in this vector space, often measured by cosine similarity. This proximity allows for various natural language processing tasks. For example, document vectors are instrumental in document clustering, where similar documents are grouped together. They are also used for document classification, enabling systems to categorize documents into predefined classes. Furthermore, document vectors facilitate efficient information retrieval, allowing search engines to find documents that are semantically related to a query, even if they don't share exact keywords. The dimensionality of these vectors can vary significantly depending on the chosen algorithm and the complexity of the corpus being analyzed.