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

[118x Apr 2020]

• Stanford CS 372 Lecture 4 NLP, Past, Present, and Future, April 2020
• Natural Language Processing in 5minutes! (Stanford nlp)
• Stanford CS224U Natural Language Understanding Spring 2019 Lecture 1 – Course Overview
• Stanford CS224U Natural Language Understanding Spring 2019 Lecture 8 – NLI 1
• Stanford CS224U Natural Language Understanding Spring 2019 Lecture 9 – NLI 2
• Stanford CS224U Natural Language Understanding Spring 2019 Lecture 7 – Relation Extraction
• Stanford CS224U Natural Language Understanding Spring 2019 Lecture 6 – Sentiment Analysis 2
• Lecture 29 — Multinomial Naive Bayes A Worked Example — [ NLP || Dan Jurafsky || Stanford ]
• Lecture 46 — Maximum Entropy Sequence Models — [ NLP || Christopher Manning || Stanford University ]
• Lecture 43 — Introduction to Information Extraction — [ NLP || Christopher Manning || Stanford ]
• Lecture 44 — Evaluation of Named Entity Recognition — [ NLP || Christopher Manning || Stanford ]
• Lecture 40 — Feature Based Linear Classifiers — [ NLP || Christopher Manning|| Stanford University ]
• Lecture 38 — Generative vs Discriminative Models — [ NLP || Christopher Manning || Stanford ]
• Lecture 37 — Other Sentiment Tasks — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 36 — Learning Sentiment Lexicons — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 35 — Sentiment Lexicons — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 34 — Sentiment Analysis A baseline algorithm— [ NLP || Dan Jurafsky || Stanford University]
• Lecture 33 — What is Sentiment Analysis — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 31 — Text Classification Evaluation — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 32 — Practical Issues in Text Classification— [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 30 — Precision, Recall, and the F measure — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 28 — Naive Bayes Relationship to Language Modeling — [ NLP || Dan Jurafsky || Stanford ]
• Lecture 27 — Naive Bayes Learning — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 26 — Formalizing the Naive Bayes Classifier — [ NLP || Dan Jurafsky || Stanford ]
• Lecture 24 — What is Text Classification — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 25 — Naive Bayes — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 23 — State of the Art Systems — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 22 — Real Word Spelling Correction — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 21 — The Noisy Channel Model of Spelling — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 20 — The Spelling Correction Task — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 15 – Presenting Your Work | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 13 – Evaluation Metrics | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 12 – Evaluation Methods | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 11 – Semantic Parsing | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 10 – Grounding | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 4 – Word Vectors 3 | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 1 – Course Overview | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 2 – Word Vectors 1 | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 3 – Word Vectors 2 | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 7 – Relation Extraction | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 6 – Sentiment Analysis 2 | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 9 – NLI 2 | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 5 – Sentiment Analysis 1 | Stanford CS224U: Natural Language Understanding | Spring 2019
• Lecture 8 – NLI 1 | Stanford CS224U: Natural Language Understanding | Spring 2019
• Stanford CS224n NLP with Deep Learning Assignment 01 Part Three Winder 2019
• Stanford CS224n NLP with Deep Learning Assignment 01 Part Two Winder 2019
• Stanford CS224n NLP with Deep Learning Assignment 01 Part One Winder 2019
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 – Translation, Seq2Seq, Attention
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 20 – Future of NLP + Deep Learning
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 6 – Language Models and RNNs
• Lecture 1 – Introduction and Word Vectors – [NLP by Stanford]
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 19 – Bias in AI
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 18 – Constituency Parsing, TreeRNNs
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 12 – Subword Models
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 16 – Coreference Resolution
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 17 – Multitask Learning
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 15 – Natural Language Generation
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 14 – Transformers and Self-Attention
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 13 – Contextual Word Embeddings
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 11 – Convolutional Networks for NLP
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 9 – Practical Tips for Projects
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 10 – Question Answering
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 7 – Vanishing Gradients, Fancy RNNs
• Stanford Seminar – Natural Language Processing for Production-Level Conversational Interfaces
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 2 – Word Vectors and Word Senses
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 3 – Neural Networks
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 5 – Dependency Parsing
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 4 – Backpropagation
• Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors
• Stanford Seminar – Training Classifiers with Natural Language Explanations
• Lecture 19 — Kneser Ney Smoothing — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 17 — Interpolation — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 16 — Smoothing Add One — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 14 — Evaluation and Perplexity — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 15 — Generalization and Zeros — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 13 — Estimating N-gram Probabilities — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 12 — Introduction to N-grams — [ NLP || Dan Jurafsky || Stanford University ]
• SoMeDi BEIA Stanford NLP Demo Video
• Lecture 09 — Backtrace for Computing Alignments — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 10 — Weighted Minimum Edit Distance — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 08 — Computing Minimum Edit Distance — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 07 — Defining Minimum Edit Distance — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 06 — Sentence Segmentation — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 05 — Word Normalization and Stemming — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 04 — Word Tokenization — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 03 — Regular Expressions in Practical NLP — [ NLP || Chris Manning || Stanford University ]
• Lecture 02 — Regular Expressions — [ NLP || Dan Jurafsky || Stanford University ]
• Lecture 01 — Course Introduction — [ NLP || Dan Jurafsky || Stanford University ]
• 2. Starting Stanford Core NLP Server and a quick view of few features in Tamil
• 1. Applications of NLP and Downloading Stanford Core NLP Server in Tamil
• 3. Brief Explanation of Annotators in Stanford Core NLP
• 3. Brief Explanation of Annotators in Stanford Core NLP in Telugu
• 2. Starting Stanford Core NLP Server and a quick view of few features
• 2. Starting Stanford Core NLP Server and a quick view of few features in Telugu
• 1. Applications of NLP and Downloading Stanford Core NLP Server
• 1. Applications of NLP and Downloading Stanford Core NLP Server in Telugu
• Java For Text Mining and NLP with Stanford NLP
• Deep-Learning and NLP :: Abigail See :: Stanford University
• 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
• CELTA Language Analysis. (using The Stanford Parser and Key to hunt out grammar)
• Regular Expressions in Practical NLP (Stanford courses).mp4
• Stanford NLP course introduction.mp4