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Positive matches

• 0.054 + - Network geometry
• 0.027 + - Science policy
• 0.023 + - Interdisciplinary research
• 0.023 + - Caffe
• 0.018 + - Industry-Academic Partnership
• 0.017 + - Public health policy
• 0.014 + - Sentence embedding
• 0.011 + - Communication
• 0.011 + - Paper/MacKay1991
• 0.011 + - Information theory
• 0.009 + - PyTorch-BigGraph
• 0.009 + - Natural language processing
• 0.008 + - Revision control
• 0.008 + - GraphSAGE
• 0.008 + - Embedded topic model
• 0.008 + - Research tools
• 0.007 + - Topic model
• 0.006 + - Scientific misconduct
• 0.005 + - Paper/Tshitoyan2019unsupervised
• 0.005 + - Edwin Thompson Jaynes

Negative matches

• 0.004 + - Softmax function
• 0.003 + - tf-idf
• 0.002 + - Paper/Hamilton2016
• 0.002 + - Sentiment analysis
• 0.001 + - Finkbeiner test
• 0.001 + - Machine learning
• 0.001 + - sense2vec
• 0.001 + - Skip-gram
• 0.001 + - Clinical concept embedding
• 0.001 + - Neural network
• 0.001 + - Deep learning
• 0.001 + - Ilya Sutskever
• 0.001 + - Paper/Levy2014a
• 0.001 + - Chris McCormick
• 0.001 + - TensorFlow
• 0.001 + - Paper/Levy2014
• 0.001 + - Gender bias
• 0.001 + - Recurrent neural network
• 0.001 + - Paper/Perozzi2014
• 0.001 + - Tomas Mikolov