Context: Topic Models and Word Embeddings · Word embedding Softmax classifier Word2vec(Nikolov et al. 13 Glove(Pennington et al. 14 Matrix factorization ∑ embedding (Deerwester 90; Levy et al 15 Projection layer the cat sits on themat Italy Mad Germany walked Berlin swam Russ⊥ walki Canada v⊥ etna Hanoi Male-Female Verb tense Country-Capital https://www.tensorflow.org/versions/ro.7/tutorials/word2vec/index.htmlContext: Topic Models and Word Embeddings • Word embedding – Word2vec (Mikolov et al., 13) – Glove (Pennington et al., 14) – Matrix factorization (Deerwester’90;Levy et al., 15) – … https://www.tensorflow.org/versions/r0.7/tutorials/word2vec/index.html 7