Tag Archives: Jurafsky

6 – 6 – Multinomial Naive Bayes_ A Worked Example – Stanford NLP-Dan Jurafsky & Chris Manning

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6-5-Naive Bayes_ Relationship to Language Modeling-Stanford NLP-Dan Jurafsky & Chris Manning

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7 – 4 – Learning Sentiment Lexicons – Stanford NLP – Professor Dan Jurafsky & Chris Manning

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4 – 2 – Estimating N-gram Probabilities – Stanford NLP – Professor Dan Jurafsky & Chris Manning

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2 – 4 – Word Normalization and Stemming – Stanford NLP – Professor Dan Jurafsky & Chris Manning

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6 – 2 – Naive Bayes – Stanford NLP – Professor Dan Jurafsky & Chris Manning

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15 – 3 – CKY Parsing -Stanford NLP-Professor Dan Jurafsky & Chris Manning

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15 – 1 – CFGs and PCFGs -Stanford NLP-Professor Dan Jurafsky & Chris Manning

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10 – 1 – What is Relation Extraction- Stanford NLP – Professor Dan Jurafsky & Chris Manning

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4 – 1 – Introduction to N-grams- Stanford NLP – Professor Dan Jurafsky & Chris Manning

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12 – 1 – An Intro to Parts of Speech and POS Tagging -NLP-Dan Jurafsky & Chris Manning

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5 – 1 – The Spelling Correction Task – Stanford NLP – Professor Dan Jurafsky & Chris Manning

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4 – 3 – Evaluation and Perplexity – Stanford NLP – Professor Dan Jurafsky & Chris Manning

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2 – 5 – Sentence Segmentation – Stanford NLP – Professor Dan Jurafsky & Chris Manning

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17 – 1 – Dependency Parsing Introduction-Stanford NLP-Professor Dan Jurafsky & Chris Manning

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2 – 3 – Word Tokenization- Stanford NLP – Professor Dan Jurafsky & Chris Manning

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7 – 1 – What is Sentiment Analysis- Stanford NLP – Professor Dan Jurafsky & Chris Manning

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21 – 5 – Advanced_ Answering Complex Questions-NLP-Dan Jurafsky & Chris Manning

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1 – 1 – Course Introduction – Stanford NLP – Professor Dan Jurafsky & Chris Manning

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