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Positive matches

• 0.041 + - Science
• 0.033 + - Linguistic diversity
• 0.025 + - Machine learning
• 0.021 + - Kullback–Leibler divergence
• 0.019 + - Interdisciplinary research
• 0.018 + - Stochastic block model
• 0.013 + - Lexical feature selection
• 0.012 + - Pajek
• 0.012 + - Community evolution
• 0.010 + - Community
• 0.009 + - Edwin Thompson Jaynes
• 0.009 + - Temporal community structure
• 0.008 + - Paper
• 0.004 + - Science policy
• 0.004 + - Dynamic community structure
• 0.004 + - Machine translation
• 0.003 + - Cross entropy
• 0.003 + - ICWSM
• 0.003 + - WebSci
• 0.003 + - KDD

Negative matches

• 0.015 + - Text analysis
• 0.002 + - Data analysis
• 0.001 + - tf-idf
• 0.001 + - NLP
• 0.000 + - StanfordNLP
• 0.000 + - Convolutional neural network
• 0.000 + - NLTK
• 0.000 + - Kyunghyun Cho
• 0.000 + - Gensim
• 0.000 + - Word embedding
• 0.000 + - spaCy
• 0.000 + - scispaCy
• 0.000 + - fastText
• 0.000 + - KenLM
• 0.000 + - Amazon Elastic Compute Cloud
• 0.000 + - Sentiment analysis
• 0.000 + - Biomedical natural language processing
• 0.000 + - word2vec
• 0.000 + - CNN
• 0.000 + - Sentence embedding