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This facet of word2vec has been exploited in a variety of other contexts. For example, word2vec has been used to map a vector space of words in one language to a vector space constructed from another language. Relationships between translated words in both spaces can be used to assist with machine translation of new words. [27]
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]
BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the ...
An example of annotating a corpus is part-of-speech tagging, or POS-tagging, in which information about each word's part of speech (verb, noun, adjective, etc.) is added to the corpus in the form of tags. Another example is indicating the lemma (base) form of each word.
There have been a number of controversies about the real-life usage of word2vec and the incorporated gender bias such as the "doctor - man = nurse" or "computer programmer - man = homemaker" examples, and I think this page should reflect some of these, even if this is a more general problem related to AI bias.
The BoW representation of a text removes all word ordering. For example, the BoW representation of "man bites dog" and "dog bites man" are the same, so any algorithm that operates with a BoW representation of text must treat them in the same way. Despite this lack of syntax or grammar, BoW representation is fast and may be sufficient for simple ...
Word2Vec: [4] Word2Vec is a popular embedding model used in natural language processing (NLP). It learns word embeddings by training a neural network on a large corpus of text. Word2Vec captures semantic and syntactic relationships between words, allowing for meaningful computations like word analogies.
An example is the extraction from newswire reports of corporate mergers, such as denoted by the formal relation: M e r g e r B e t w e e n ( c o m p a n y 1 , c o m p a n y 2 , d a t e ) {\displaystyle \mathrm {MergerBetween} (company_{1},company_{2},date)} ,