Search "+Diversity -Paper +Paper/Iacopini2018 +Paper/Levy2014a -Paper/McNerney2018production -Tomas Mikolov -Embedded topic model -Topic mapping +Hierarchical softmax"

Positive matches

• 0.148 + - Innovation
• 0.078 + - Softmax
• 0.065 + - Basic research
• 0.056 + - Softmax function
• 0.037 + - Heaps' law
• 0.036 + - Innovation hot spots
• 0.029 + - Omer Levy
• 0.027 + - Word embedding
• 0.026 + - Yoav Goldberg
• 0.026 + - word2vec
• 0.005 + - wiki2vec
• 0.003 + - Creative work
• 0.003 + - Meme
• 0.003 + - Andreas Wagner
• 0.003 + - William Rosen
• 0.003 + - Diffusion of innovations
• 0.002 + - Deep generative models
• 0.002 + - Bias
• 0.002 + - Skip-gram
• 0.002 + - Sentiment analysis

Negative matches

• 0.100 + - Wassily Leontief
• 0.093 + - Topic model
• 0.057 + - Continuous embedding
• 0.056 + - Recurrent neural network
• 0.044 + - Topic modeling
• 0.041 + - Natural language processing
• 0.040 + - LDA
• 0.027 + - Sentence embedding
• 0.016 + - Input–output model
• 0.016 + - Doyne Farmer
• 0.016 + - Charles Savoie
• 0.016 + - Economic network
• 0.016 + - Francesco Caravelli
• 0.016 + - James McNerney
• 0.014 + - Graph embedding
• 0.013 + - RNN
• 0.010 + - Computational creativity
• 0.006 + - Attention (Deep learning)
• 0.006 + - Technological unemployment
• 0.005 + - StanfordNLP