<斯坦福NLP课程> ├<1> │ └1 - 1 - Course Introduction - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 ├<10> │ ├._10 - 1 - What is Relation Extraction- Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├._10 - 2 - Using Patterns to Extract Relations - Stanford NLP - Professor Dan Jurafsky & Chris Mann │ ├._10 - 3 - Supervised Relation Extraction - Stanford NLP - Professor Dan Jurafsky & Chris Manning.m │ ├._10 - 4 - Semi-Supervised and Unsupervised Relation Extraction-Dan Jurafsky & Chris Manning.mp4 │ ├10 - 1 - What is Relation Extraction- Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├10 - 2 - Using Patterns to Extract Relations - Stanford NLP - Professor Dan Jurafsky & Chris Mannin │ ├10 - 3 - Supervised Relation Extraction - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ └10 - 4 - Semi-Supervised and Unsupervised Relation Extraction-Dan Jurafsky & Chris Manning.mp4 ├<11> │ ├._11 - 1 - The Maximum Entropy Model Presentation-NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._11 - 2 - Feature Overlap_Feature Interaction-Stanford NLP-Professor Dan Jurafsky & Chris Manning │ ├._11 - 3 - Conditional Maxent Models for Classification--NLP-Dan Jurafsky & Chris Manning.mp4 │ ├._11 - 4 - Smoothing_Regularization_Priors for Maxent Models-NLP-Dan Jurafsky & Chris Manning - You │ ├11 - 1 - The Maximum Entropy Model Presentation-NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├11 - 2 - Feature Overlap_Feature Interaction-Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├11 - 3 - Conditional Maxent Models for Classification--NLP-Dan Jurafsky & Chris Manning.mp4 │ └11 - 4 - Smoothing_Regularization_Priors for Maxent Models-NLP-Dan Jurafsky & Chris Manning - YouTu ├<12> │ ├._12 - 1 - An Intro to Parts of Speech and POS Tagging -NLP-Dan Jurafsky & Chris Manning.mp4 │ ├._12 - 2 - Some Methods and Results on Sequence Models for POS Tagging -Dan Jurafsky Chris Manning │ ├12 - 1 - An Intro to Parts of Speech and POS Tagging -NLP-Dan Jurafsky & Chris Manning.mp4 │ └12 - 2 - Some Methods and Results on Sequence Models for POS Tagging -Dan Jurafsky Chris Manning - ├<13> │ ├._13 - 1 - Syntactic Structure_ Constituency vs Dependency -NLP-Dan Jurafsky & Chris Manning - YouT │ ├._13 - 2 - Empirical_Data-Driven Approach to Parsing-NLP-Dan Jurafsky & Chris Manning.mp4 │ ├._13 - 3 The Exponential Problem in Parsing-NLP-Dan Jurafsky & Chris Manning.mp4 │ ├13 - 1 - Syntactic Structure_ Constituency vs Dependency -NLP-Dan Jurafsky & Chris Manning - YouTub │ ├13 - 2 - Empirical_Data-Driven Approach to Parsing-NLP-Dan Jurafsky & Chris Manning.mp4 │ └13 - 3 The Exponential Problem in Parsing-NLP-Dan Jurafsky & Chris Manning.mp4 ├<14> │ └14 -1-Instructor Chat --NLP-Dan Jurafsky & Chris Manning.mp4 ├<15> │ ├._15 - 1 - CFGs and PCFGs -Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._15 - 2 - Grammar Transforms-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._15 - 3 - CKY Parsing -Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._15 - 4 - CKY Example-Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├._15 - 5 - Constituency Parser Evaluation -Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├15 - 1 - CFGs and PCFGs -Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├15 - 2 - Grammar Transforms-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├15 - 3 - CKY Parsing -Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├15 - 4 - CKY Example-Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ └15 - 5 - Constituency Parser Evaluation -Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 ├<16> │ ├._16 - 1 - Lexicalization of PCFGs-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._16 - 2 - Charniak's Model-Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├._16 - 3 - PCFG Independence Assumptions-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouT │ ├._16 - 4 - The Return of Unlexicalized PCFGs-Stanford NLP-Professor Dan Jurafsky & Chris Manning - │ ├._16 - 5 - Latent Variable PCFGs-Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├16 - 1 - Lexicalization of PCFGs-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├16 - 2 - Charniak's Model-Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├16 - 3 - PCFG Independence Assumptions-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTub │ ├16 - 4 - The Return of Unlexicalized PCFGs-Stanford NLP-Professor Dan Jurafsky & Chris Manning - Yo │ └16 - 5 - Latent Variable PCFGs-Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 ├<17> │ ├._17 - 1 - Dependency Parsing Introduction-Stanford NLP-Professor Dan Jurafsky & Chris Manning - Yo │ ├._17 - 2 - Greedy Transition-Based Parsing-Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├._17 - 3 - Dependencies Encode Relational Structure-Stanford NLP-Dan Jurafsky & Chris Manning - You │ ├17 - 1 - Dependency Parsing Introduction-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouT │ ├17 - 2 - Greedy Transition-Based Parsing-Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ └17 - 3 - Dependencies Encode Relational Structure-Stanford NLP-Dan Jurafsky & Chris Manning - YouTu ├<18> │ ├._18 - 1 - Introduction to Information Retrieval-Stanford NLP-Professor Dan Jurafsky & Chris Mannin │ ├._18 - 2 - Term-Document Incidence Matrices -Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├._18 - 3 - The Inverted Index-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._18 - 4 - Query Processing with the Inverted Index-Stanford NLP-Dan Jurafsky & Chris Manning - You │ ├._18 - 5 - Phrase Queries and Positional Indexes-Stanford NLP-Professor Dan Jurafsky & Chris Mannin │ ├18 - 1 - Introduction to Information Retrieval-Stanford NLP-Professor Dan Jurafsky & Chris Manning │ ├18 - 2 - Term-Document Incidence Matrices -Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├18 - 3 - The Inverted Index-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├18 - 4 - Query Processing with the Inverted Index-Stanford NLP-Dan Jurafsky & Chris Manning - YouTu │ └18 - 5 - Phrase Queries and Positional Indexes-Stanford NLP-Professor Dan Jurafsky & Chris Manning ├<19> │ ├._19 - 1 - Introducing Ranked Retrieval-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTu │ ├._19 - 2 - Scoring with the Jaccard Coefficient-Stanford NLP-Professor Dan Jurafsky & Chris Manning │ ├._19 - 3 - Term Frequency Weighting-Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├._19 - 4 - Inverse Document Frequency Weighting-Stanford NLP-Professor Dan Jurafsky & Chris Manning │ ├._19 - 5 - TF-IDF Weighting-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._19 - 6 - The Vector Space Model -Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├._19 - 7 - Calculating TF-IDF Cosine Scores-Stanford NLP-Professor Dan Jurafsky & Chris Manning - Y │ ├._19 - 8 - Evaluating Search Engines -Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├19 - 1 - Introducing Ranked Retrieval-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube │ ├19 - 2 - Scoring with the Jaccard Coefficient-Stanford NLP-Professor Dan Jurafsky & Chris Manning.m │ ├19 - 3 - Term Frequency Weighting-Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├19 - 4 - Inverse Document Frequency Weighting-Stanford NLP-Professor Dan Jurafsky & Chris Manning - │ ├19 - 5 - TF-IDF Weighting-Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├19 - 6 - The Vector Space Model -Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 │ ├19 - 7 - Calculating TF-IDF Cosine Scores-Stanford NLP-Professor Dan Jurafsky & Chris Manning - You │ └19 - 8 - Evaluating Search Engines -Stanford NLP-Professor Dan Jurafsky & Chris Manning.mp4 ├<2> │ ├._2 - 1 - Regular Expressions - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├._2 - 2 - Regular Expressions in Practical NLP - Stanford NLP - Professor Dan Jurafsky & Chris Mann │ ├._2 - 3 - Word Tokenization- Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├._2 - 4 - Word Normalization and Stemming - Stanford NLP - Professor Dan Jurafsky & Chris Manning.m │ ├._2 - 5 - Sentence Segmentation - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.m │ ├2 - 1 - Regular Expressions - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├2 - 2 - Regular Expressions in Practical NLP - Stanford NLP - Professor Dan Jurafsky & Chris Mannin │ ├2 - 3 - Word Tokenization- Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├2 - 4 - Word Normalization and Stemming - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ └2 - 5 - Sentence Segmentation - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.mp4 ├<20> │ ├._20 - 1 - Word Senses and Word Relations-NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._20 - 2 - WordNet and Other Online Thesauri -NLP-Dan Jurafsky & Chris Manning.mp4 │ ├._20 - 3 - Word Similarity and Thesaurus Methods -NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._20 - 4 - Word Similarity_ Distributional Similarity I --NLP-Dan Jurafsky & Chris Manning - YouTub │ ├._20 - 5 - Word Similarity_ Distributional Similarity II -NLP-Dan Jurafsky & Chris Manning.mp4 │ ├20 - 1 - Word Senses and Word Relations-NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├20 - 2 - WordNet and Other Online Thesauri -NLP-Dan Jurafsky & Chris Manning.mp4 │ ├20 - 3 - Word Similarity and Thesaurus Methods -NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├20 - 4 - Word Similarity_ Distributional Similarity I --NLP-Dan Jurafsky & Chris Manning - YouTube │ └20 - 5 - Word Similarity_ Distributional Similarity II -NLP-Dan Jurafsky & Chris Manning.mp4 ├<21> │ ├._21 - 1 - What is Question Answering-NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._21 - 2 - Answer Types and Query Formulation-NLP-Dan Jurafsky & Chris Manning.mp4 │ ├._21 - 3 - Passage Retrieval and Answer Extraction-NLP-Dan Jurafsky & Chris Manning.mp4 │ ├._21 - 4 - Using Knowledge in QA -NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._21 - 5 - Advanced_ Answering Complex Questions-NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├21 - 1 - What is Question Answering-NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├21 - 2 - Answer Types and Query Formulation-NLP-Dan Jurafsky & Chris Manning.mp4 │ ├21 - 3 - Passage Retrieval and Answer Extraction-NLP-Dan Jurafsky & Chris Manning.mp4 │ ├21 - 4 - Using Knowledge in QA -NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ └21 - 5 - Advanced_ Answering Complex Questions-NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 ├<22> │ ├._22 - 1 - Introduction to Summarization-NLP-Dan Jurafsky & Chris Manning.mp4 │ ├._22 - 2 - Generating Snippets-NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._22 - 3 - Evaluating Summaries_ ROUGE-NLP-Dan Jurafsky & Chris Manning.mp4 │ ├._22 - 4 - Summarizing Multiple Documents-NLP-Dan Jurafsky & Chris Manning.mp4 │ ├22 - 1 - Introduction to Summarization-NLP-Dan Jurafsky & Chris Manning.mp4 │ ├22 - 2 - Generating Snippets-NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├22 - 3 - Evaluating Summaries_ ROUGE-NLP-Dan Jurafsky & Chris Manning.mp4 │ └22 - 4 - Summarizing Multiple Documents-NLP-Dan Jurafsky & Chris Manning.mp4 ├<23> │ └23 - 1 - Instructor Chat II -Stanford NLP-Professor Dan Jurafsky & Chris Manning - YouTube.mp4 ├<3> │ ├._3 - 1 - Defining Minimum Edit Distance - Stanford NLP - Professor Dan Jurafsky & Chris Manning - │ ├._3 - 2 - Computing Minimum Edit Distance - Stanford NLP - Professor Dan Jurafsky & Chris Manning.m │ ├._3 - 3 - Backtrace for Computing Alignments - Stanford NLP - Professor Dan Jurafsky & Chris Mannin │ ├._3 - 4 - Weighted Minimum Edit Distance - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├._3 - 5-Minimum Edit Distance in Computational Biology-Stanford NLP-Dan Jurafsky & Chris Manning - │ ├3 - 1 - Defining Minimum Edit Distance - Stanford NLP - Professor Dan Jurafsky & Chris Manning - Yo │ ├3 - 2 - Computing Minimum Edit Distance - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├3 - 3 - Backtrace for Computing Alignments - Stanford NLP - Professor Dan Jurafsky & Chris Manning │ ├3 - 4 - Weighted Minimum Edit Distance - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ └3 - 5-Minimum Edit Distance in Computational Biology-Stanford NLP-Dan Jurafsky & Chris Manning - Yo ├<4> │ ├._4 - 1 - Introduction to N-grams- Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube │ ├._4 - 2 - Estimating N-gram Probabilities - Stanford NLP - Professor Dan Jurafsky & Chris Manning - │ ├._4 - 3 - Evaluation and Perplexity - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├._4 - 4 - Generalization and Zeros - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTub │ ├._4 - 5 - Smoothing_ Add-One - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube 1.mp4 │ ├._4 - 6 - Interpolation - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._4 - 7 - Good-Turing Smoothing - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.m │ ├._4 - 8 - Kneser-Ney Smoothing - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├4 - 1 - Introduction to N-grams- Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├4 - 2 - Estimating N-gram Probabilities - Stanford NLP - Professor Dan Jurafsky & Chris Manning - Y │ ├4 - 3 - Evaluation and Perplexity - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├4 - 4 - Generalization and Zeros - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube │ ├4 - 5 - Smoothing_ Add-One - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube 1.mp4 │ ├4 - 6 - Interpolation - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├4 - 7 - Good-Turing Smoothing - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ └4 - 8 - Kneser-Ney Smoothing - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.mp4 ├<5> │ ├._5 - 1 - The Spelling Correction Task - Stanford NLP - Professor Dan Jurafsky & Chris Manning - Yo │ ├._5 - 2 - The Noisy Channel Model of Spelling - Stanford NLP - Professor Dan Jurafsky & Chris Manni │ ├._5 - 3 - Real-Word Spelling Correction - Stanford NLP - Professor Dan Jurafsky & Chris Manning - Y │ ├._5 - 4 - State of the Art Systems - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTub │ ├5 - 1 - The Spelling Correction Task - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouT │ ├5 - 2 - The Noisy Channel Model of Spelling - Stanford NLP - Professor Dan Jurafsky & Chris Manning │ ├5 - 3 - Real-Word Spelling Correction - Stanford NLP - Professor Dan Jurafsky & Chris Manning - You │ └5 - 4 - State of the Art Systems - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube ├<6> │ ├._6 - 1 - What is Text Classification- Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouT │ ├._6 - 2 - Naive Bayes - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├._6 - 3 - Formalizing the Naive Bayes Classifier - Stanford NLP-Dan Jurafsky & Chris Manning.mp4 │ ├._6 - 4 - Naive Bayes_ Learning - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.m │ ├._6 - 5-Naive Bayes_ Relationship to Language Modeling-Stanford NLP-Dan Jurafsky & Chris Manning.mp4 │ ├._6 - 6 - Multinomial Naive Bayes_ A Worked Example - Stanford NLP-Dan Jurafsky & Chris Manning - Y │ ├._6 - 7 - Precision, Recall, and the F measure - Stanford NLP - Professor Dan Jurafsky & Chris Mann │ ├._6 - 8 - Text Classification_ Evaluation- Stanford NLP - Professor Dan Jurafsky & Chris Manning - │ ├._6 - 9 - Practical Issues in Text Classification - Stanford NLP-Dan Jurafsky & Chris Manning - You │ ├6 - 1 - What is Text Classification- Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTub │ ├6 - 2 - Naive Bayes - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├6 - 3 - Formalizing the Naive Bayes Classifier - Stanford NLP-Dan Jurafsky & Chris Manning.mp4 │ ├6 - 4 - Naive Bayes_ Learning - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├6 - 5-Naive Bayes_ Relationship to Language Modeling-Stanford NLP-Dan Jurafsky & Chris Manning.mp4 │ ├6 - 6 - Multinomial Naive Bayes_ A Worked Example - Stanford NLP-Dan Jurafsky & Chris Manning - You │ ├6 - 7 - Precision, Recall, and the F measure - Stanford NLP - Professor Dan Jurafsky & Chris Mannin │ ├6 - 8 - Text Classification_ Evaluation- Stanford NLP - Professor Dan Jurafsky & Chris Manning - Yo │ └6 - 9 - Practical Issues in Text Classification - Stanford NLP-Dan Jurafsky & Chris Manning - YouTu ├<7> │ ├._7 - 1 - What is Sentiment Analysis- Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTu │ ├._7 - 2 - Sentiment Analysis_ A baseline algorithm- NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._7 - 3 - Sentiment Lexicons - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._7 - 4 - Learning Sentiment Lexicons - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├._7 - 5 - Other Sentiment Tasks - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├7 - 1 - What is Sentiment Analysis- Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube │ ├7 - 2 - Sentiment Analysis_ A baseline algorithm- NLP-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├7 - 3 - Sentiment Lexicons - Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├7 - 4 - Learning Sentiment Lexicons - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ └7 - 5 - Other Sentiment Tasks - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 ├<8> │ ├._8 - 1 - Generative vs Discriminative Models- Stanford NLP - Professor Dan Jurafsky & Chris Mannin │ ├._8 - 2 - Making features from text for discriminative NLP models-Dan Jurafsky & Chris Manning - Yo │ ├._8 - 3 - Feature-Based Linear Classifiers - Stanford NLP - Professor Dan Jurafsky & Chris Manning │ ├._8 - 4 - Building a Maxent Model_ The Nuts and Bolts-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├._8 - 5 - Generative vs Discriminative models_ The problem of overcounting evidence- Stanford NLP - │ ├._8 - 6 - Maximizing the Likelihood- Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTub │ ├8 - 1 - Generative vs Discriminative Models- Stanford NLP - Professor Dan Jurafsky & Chris Manning │ ├8 - 2 - Making features from text for discriminative NLP models-Dan Jurafsky & Chris Manning - YouT │ ├8 - 3 - Feature-Based Linear Classifiers - Stanford NLP - Professor Dan Jurafsky & Chris Manning.mp4 │ ├8 - 4 - Building a Maxent Model_ The Nuts and Bolts-Dan Jurafsky & Chris Manning - YouTube.mp4 │ ├8 - 5 - Generative vs Discriminative models_ The problem of overcounting evidence- Stanford NLP - Y │ └8 - 6 - Maximizing the Likelihood- Stanford NLP - Professor Dan Jurafsky & Chris Manning - YouTube ├<9> │ ├._9 - 1 - Introduction to Information Extraction- Stanford NLP-Dan Jurafsky & Chris Manning.mp4 │ ├._9 - 2 - Evaluation of Named Entity Recognition- Stanford NLP-Dan Jurafsky & Chris Manning - YouTu │ ├._9 - 3 - Sequence Models for Named Entity Recognition-NLP-Professor Dan Jurafsky & Chris Manning - │ ├._9 - 4 - Maximum Entropy Sequence Models- Stanford NLP - Professor Dan Jurafsky & Chris Manning - │ ├9 - 1 - Introduction to Information Extraction- Stanford NLP-Dan Jurafsky & Chris Manning.mp4 │ ├9 - 2 - Evaluation of Named Entity Recognition- Stanford NLP-Dan Jurafsky & Chris Manning - YouTube │ ├9 - 3 - Sequence Models for Named Entity Recognition-NLP-Professor Dan Jurafsky & Chris Manning - Y │ └9 - 4 - Maximum Entropy Sequence Models- Stanford NLP - Professor Dan Jurafsky & Chris Manning - Yo
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