Notes:
Stanford NLP (Natural Language Processing) is a suite of natural language processing tools developed by the Stanford NLP Group at Stanford University. It is widely used in the field of natural language processing, which involves using computers to understand, analyze, and generate human language.
The Stanford NLP suite includes a number of tools and resources for natural language processing, including:
- Tokenization: A tool for breaking down text into smaller units called tokens, such as words or punctuation.
- Part-of-speech tagging: A tool for identifying the part of speech of each word in a text, such as nouns, verbs, and adjectives.
- Named entity recognition: A tool for identifying and classifying named entities in text, such as people, organizations, and locations.
- Parsing: A tool for analyzing the grammatical structure of a sentence, including the relationships between words and phrases.
- Dependency parsing: A tool for analyzing the dependencies between words in a sentence, such as the relationship between a noun and its modifying adjective.
The Stanford NLP suite is used in a variety of applications, including information retrieval, machine translation, question answering, and text summarization. It is also used in research and development of natural language processing technologies.
Resources:
- nlp.stanford.edu .. the natural language processing group at stanford university
Wikipedia:
See also:
Stanford NLP & Dialog Systems | Stanford Parser & Dialog Systems
- 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