Meta-Guide.com

Resources:

• nlp.stanford.edu .. the natural language processing group at stanford university

Wikipedia:

• Outline of natural language processing

See also:

Stanford NLP & Dialog Systems | Stanford Parser & Dialog Systems

[87x Sep 2017]

• Stanford Core NLP Java Example | Natural Language Processing
• What is NLP? What is Stanford Core NLP?
• 18 2 Term Document Incidence Matrices Stanford NLP Professor Dan Jurafsky & Chris Manning You
• 18 3 The Inverted Index Stanford NLP Professor Dan Jurafsky & Chris Manning YouTube
• 18 1 Introduction to Information Retrieval Stanford NLP Professor Dan Jurafsky & Chris Manning
• 18 4 Query Processing with the Inverted Index Stanford NLP Dan Jurafsky & Chris Manning YouTub
• 18 5 Phrase Queries and Positional Indexes Stanford NLP Professor Dan Jurafsky & Chris Manning
• Relationship Extraction from Unstructured Text Based on Stanford NLP with Spark
• NLP/Text Analytics: Spark ML & Pipelines, Stanford CoreNLP, Succint, KeystoneML (Part 1)
• NLP/Text Analytics: Spark ML & Pipelines, Stanford CoreNLP, Succint, KeystoneML (Part 2)
• NLP-Langmaster-Stanford
• 23 – 1 – Instructor Chat II -Stanford NLP-Professor Dan Jurafsky & Chris Manning
• CELTA Language Analysis. (using The Stanford Parser and Key to hunt out grammar)
• 19 – 8 – Evaluating Search Engines -Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 19 – 7 – Calculating TF-IDF Cosine Scores-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 19 – 6 – The Vector Space Model -Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 19 – 5 – TF-IDF Weighting-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 19 – 4 – Inverse Document Frequency Weighting-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 19 – 3 – Term Frequency Weighting-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 18 – 5 – Phrase Queries and Positional Indexes-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 19 – 2 – Scoring with the Jaccard Coefficient-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 19 – 1 – Introducing Ranked Retrieval-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 18 – 4 – Query Processing with the Inverted Index-Stanford NLP-Dan Jurafsky & Chris Manning
• 18 – 3 – The Inverted Index-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 18 – 2 – Term-Document Incidence Matrices -Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 18 – 1 – Introduction to Information Retrieval-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 17 – 2 – Greedy Transition-Based Parsing-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 17 – 3 – Dependencies Encode Relational Structure-Stanford NLP-Dan Jurafsky & Chris Manning
• 17 – 1 – Dependency Parsing Introduction-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 16 – 5 – Latent Variable PCFGs-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 16 – 4 – The Return of Unlexicalized PCFGs-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 16 – 2 – Charniak’s Model-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 16 – 3 – PCFG Independence Assumptions-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 16 – 1 – Lexicalization of PCFGs-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 15 – 4 – CKY Example-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 15 – 5 – Constituency Parser Evaluation -Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 15 – 3 – CKY Parsing -Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 15 – 2 – Grammar Transforms-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 15 – 1 – CFGs and PCFGs -Stanford NLP-Professor Dan Jurafsky & Chris Manning
• Regular Expressions in Practical NLP (Stanford courses).mp4
• Stanford NLP course introduction.mp4
• 11 – 2 – Feature Overlap_Feature Interaction-Stanford NLP-Professor Dan Jurafsky & Chris Manning
• 10 – 3 – Supervised Relation Extraction – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 10 – 2 – Using Patterns to Extract Relations – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 10 – 1 – What is Relation Extraction- Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 9 – 4 – Maximum Entropy Sequence Models- Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 9 – 2 – Evaluation of Named Entity Recognition- Stanford NLP-Dan Jurafsky & Chris Manning
• 9 – 1 – Introduction to Information Extraction- Stanford NLP-Dan Jurafsky & Chris Manning
• 8 – 6 – Maximizing the Likelihood- Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 8 – 3 – Feature-Based Linear Classifiers – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 8 – 1 – Generative vs. Discriminative Models- Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 7 – 5 – Other Sentiment Tasks – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 7 – 4 – Learning Sentiment Lexicons – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 7 – 3 – Sentiment Lexicons – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 7 – 1 – What is Sentiment Analysis- Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 6 – 9 – Practical Issues in Text Classification – Stanford NLP-Dan Jurafsky & Chris Manning
• 6 – 8 – Text Classification_ Evaluation- Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 6 – 7 – Precision, Recall, and the F measure – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 6 – 6 – Multinomial Naive Bayes_ A Worked Example – Stanford NLP-Dan Jurafsky & Chris Manning
• 6-5-Naive Bayes_ Relationship to Language Modeling-Stanford NLP-Dan Jurafsky & Chris Manning
• 6 – 4 – Naive Bayes_ Learning – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 6 – 3 – Formalizing the Naive Bayes Classifier – Stanford NLP-Dan Jurafsky & Chris Manning
• 6 – 2 – Naive Bayes – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 6 – 1 – What is Text Classification- Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 5 – 4 – State of the Art Systems – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 5 – 3 – Real-Word Spelling Correction – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 5 – 2 – The Noisy Channel Model of Spelling – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 5 – 1 – The Spelling Correction Task – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 4 – 8 – Kneser-Ney Smoothing – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 4 – 7 – Good-Turing Smoothing – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 4 – 6 – Interpolation – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 4 – 5 – Smoothing_ Add-One – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 4 – 4 – Generalization and Zeros – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 4 – 3 – Evaluation and Perplexity – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 4 – 2 – Estimating N-gram Probabilities – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 4 – 1 – Introduction to N-grams- Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 3-5-Minimum Edit Distance in Computational Biology-Stanford NLP-Dan Jurafsky & Chris Manning
• 3 – 4 – Weighted Minimum Edit Distance – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 3 – 3 – Backtrace for Computing Alignments – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 3 – 2 – Computing Minimum Edit Distance – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 3 – 1 – Defining Minimum Edit Distance – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 2 – 5 – Sentence Segmentation – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 2 – 4 – Word Normalization and Stemming – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 2 – 3 – Word Tokenization- Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 2 – 2 – Regular Expressions in Practical NLP – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 2 – 1 – Regular Expressions – Stanford NLP – Professor Dan Jurafsky & Chris Manning
• 1 – 1 – Course Introduction – Stanford NLP – Professor Dan Jurafsky & Chris Manning