ordvektorene

ordvektorene is a Norwegian term that translates to "word vectors" in English. It refers to a method in natural language processing (NLP) where words are represented as dense numerical vectors in a multi-dimensional space. These vectors capture semantic relationships between words, meaning that words with similar meanings tend to be located closer to each other in this vector space. The process of generating word vectors typically involves training a neural network on a large corpus of text. During training, the network learns to predict a word based on its context or vice versa. The internal representations learned by the network, specifically the weights associated with each word, are then used as the word vectors. Popular algorithms for generating word vectors include Word2Vec (both Skip-gram and CBOW models) and GloVe. The resulting word vectors can be used in a wide range of NLP tasks, such as sentiment analysis, machine translation, text classification, and question answering, by providing a way to quantify and utilize the meaning of words computationally.