This tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These representations can be subsequently used in many natural language processing applications and for further research. The word2vec tool takes a text corpus as input and produces the word vectors as output. It first constructs a vocabulary from the training text data and then learns vector representation of words. The resulting word vector file can be used as features in many natural language processing and machine learning applications. ..

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  1. Baechler, Gilles; Dümbgen, Frederike; Elhami, Golnoosh; Kreković, Miranda; Vetterli, Martin: Coordinate difference matrices (2020)
  2. Bassu, Devasis; Jones, Peter W.; Ness, Linda; Shallcross, David: Product formalisms for measures on spaces with binary tree structures: representation, visualization, and multiscale noise (2020)
  3. Derbanosov, R. Yu.; Irkhin, I. A.: Issues of stability and uniqueness of stochastic matrix factorization (2020)
  4. Fangzhou Xie: Pruned Wasserstein Index Generation Model and wigpy Package (2020) arXiv
  5. Fürnkranz, Johannes; Kliegr, Tomáš; Paulheim, Heiko: On cognitive preferences and the plausibility of rule-based models (2020)
  6. Grigorieva, Elena Gennadievna; Klyachin, Vladimir Aleksandrovich: The study of the statistical characteristics of the text based on the graph model of the linguistic corpus (2020)
  7. Ito, Tomoki; Tsubouchi, Kota; Sakaji, Hiroki; Yamashita, Tatsuo; Izumi, Kiyoshi: Concept cloud-based sentiment visualization for financial reviews (2020)
  8. Kazemi, Seyed Mehran; Goel, Rishab; Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal: Representation learning for dynamic graphs: a survey (2020)
  9. Kreĭnes, M. G.; Kreĭnes, Elena M.: Matrix text models. Text models and similarity of text contents (2020)
  10. Kreĭnes, M. G.; Kreĭnes, Elena M.: Matrix text models. Text corpora models (2020)
  11. Kutlu, Mucahid; McDonnell, Tyler; Elsayed, Tamer; Lease, Matthew: Annotator rationales for labeling tasks in crowdsourcing (2020)
  12. Lavrač, Nada; Škrlj, Blaž; Robnik-Šikonja, Marko: Propositionalization and embeddings: two sides of the same coin (2020)
  13. Lee, Gee Y.; Manski, Scott; Maiti, Tapabrata: Actuarial applications of word embedding models (2020)
  14. Lee, O-Joun; Jung, Jason J.: Story embedding: learning distributed representations of stories based on character networks (2020)
  15. Liberti, Leo: Distance geometry and data science (2020)
  16. Li, Dandan; Summers-Stay, Douglas: Dual embeddings and metrics for word and relational similarity (2020)
  17. Nguyen, Thi Thanh Sang; Do, Pham Minh Thu: Classification optimization for training a large dataset with naïve Bayes (2020)
  18. Pasini, Tommaso; Navigli, Roberto: Train-o-matic: supervised word sense disambiguation with no (manual) effort (2020)
  19. Pio, Gianvito; Ceci, Michelangelo; Prisciandaro, Francesca; Malerba, Donato: Exploiting causality in gene network reconstruction based on graph embedding (2020)
  20. Ruiz, Francisco J. R.; Athey, Susan; Blei, David M.: SHOPPER: a probabilistic model of consumer choice with substitutes and complements (2020)

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