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2. Word2vec - Wikipedia

   en.wikipedia.org/wiki/Word2vec

   Word2vec was developed by Tomáš Mikolov and colleagues at Google and published in 2013. 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 .

3. Tomáš Mikolov - Wikipedia

   en.wikipedia.org/wiki/Tomáš_Mikolov

   Mikolov obtained his PhD in Computer Science from Brno University of Technology for his work on recurrent neural network-based language models. [1] [2] He is the lead author of the 2013 paper that introduced the Word2vec technique in natural language processing [3] and is an author on the FastText architecture.

4. Word embedding - Wikipedia

   en.wikipedia.org/wiki/Word_embedding

   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]

5. Attention Is All You Need - Wikipedia

   en.wikipedia.org/wiki/Attention_Is_All_You_Need

   In language modelling, ELMo (2018) was a bi-directional LSTM that produces contextualized word embeddings, improving upon the line of research from bag of words and word2vec. It was followed by BERT (2018), an encoder-only Transformer model. [33] In 2019 October, Google started using BERT to process search queries. [34]

6. MuPDF - Wikipedia

   en.wikipedia.org/wiki/MuPDF

   MuPDF is a free and open-source software framework written in C that implements a PDF, XPS, and EPUB parsing and rendering engine. It is used primarily to render pages into bitmaps , but also provides support for other operations such as searching and listing the table of contents and hyperlinks.

7. Latent space - Wikipedia

   en.wikipedia.org/wiki/Latent_space

   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.