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Positive matches

• 0.051 + - Lexical feature selection
• 0.025 + - Text analysis
• 0.020 + - Word embedding
• 0.019 + - Industry-Academic Partnership
• 0.019 + - Machine learning
• 0.017 + - Harold Lasswell
• 0.017 + - Public health policy
• 0.017 + - Paper/Nielsen2017
• 0.017 + - Probability theory
• 0.015 + - Migration
• 0.013 + - Research funding
• 0.012 + - Natural language processing
• 0.012 + - Graph embedding
• 0.009 + - Edwin Thompson Jaynes
• 0.008 + - Hessian matrix
• 0.008 + - Paper
• 0.006 + - Data analysis
• 0.005 + - NLP
• 0.005 + - Collaborative software
• 0.005 + - Network geometry

Negative matches

• 0.012 + - Residual leanrning
• 0.007 + - Paper/Gatys2015
• 0.006 + - Residual Learning
• 0.006 + - Deep learning
• 0.004 + - CNN
• 0.003 + - Deep generative models
• 0.001 + - Artificial neural network
• 0.001 + - Meta learning
• 0.001 + - KoNLPy
• 0.001 + - Long short-term memory
• 0.001 + - Latent Dirichlet allocation
• 0.001 + - Dialog act
• 0.001 + - Christopher D. Manning
• 0.001 + - Dropout
• 0.001 + - Word segmentation
• 0.001 + - Sentiment analysis
• 0.000 + - Generative adversarial networks
• 0.000 + - Brain and deep learning
• 0.000 + - Theano
• 0.000 + - Paper/Cooijmans2016