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Word2vec represents a word as a high-dimension vector of numbers which capture relationships between words. In particular, words which appear in similar contexts are mapped to vectors which are nearby as measured by cosine similarity.
The bag-of-words model (BoW) is a model of text which uses a representation of text that is based on an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity.
A thesaurus (pl.: thesauri or thesauruses), sometimes called a synonym dictionary or dictionary of synonyms, is a reference work which arranges words by their meanings (or in simpler terms, a book where one can find different words with similar meanings to other words), [1] [2] sometimes as a hierarchy of broader and narrower terms, sometimes simply as lists of synonyms and antonyms.
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
The word calque is a loanword, while the word loanword is a calque: calque comes from the French noun calque ("tracing; imitation; close copy"); [5] while the word loanword and the phrase loan translation are translated from German nouns Lehnwort [6] and Lehnübersetzung (German: [ˈleːnʔybɐˌzɛt͡sʊŋ] ⓘ). [7]
A single tree can capture up to 250 tons in its lifetime, the equivalent of removing nearly 200 cars from the road for an entire year. But between logging and fires, 95% of California's redwoods ...
In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model [1] [2] can be applied to image classification or retrieval, by treating image features as words. In document classification , a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary.
Animation of the topic detection process in a document-word matrix. Every column corresponds to a document, every row to a word. A cell stores the weighting of a word in a document (e.g. by tf-idf), dark cells indicate high weights. LSA groups both documents that contain similar words, as well as words that occur in a similar set of documents.