Tag Archives: A Simple Prediction

[repost ]Artificial Intelligence | Natural Language Processing (Stanford Engineering Everywhere)

 

original:http://see.stanford.edu/see/lecturelist.aspx?coll=63480b48-8819-4efd-8412-263f1a472f5a

COURSE CONTENT:

 Course Home
 Lectures
 Syllabus
 Assignments
 Exams

Course Meetings: 18

Lecture 1    View Now >
1 hr 13 min*

  • Topics: Logistics, Goals Of The Field Of NLP, Is The Problem Just Cycles?, Why NLP Is Difficult? The Hidden Structure Of Language, Why NLP Is Difficult: Newspaper Headlines, Machine Translation, Machine Translation History, Centauri/Arcturan Example
  • Transcript: HTML PDF

 

Lecture 2    View Now >
1 hr 14 min*

  • Topics: Questions That Linguistics Should Answer, Machine Translation (MT), Probabilistic Language Models, Evaluation, Sparsity, Smoothing, How Much Mass To Withhold?
  • Transcript: HTML PDF

 

Lecture 3    View Now >
1 hr 15 min

  • Topics: Finish Smoothing From Last Lecture, Kneser-Ney Smoothing, Practical Considerations, Machine Translation (Lecture 3), Tokenization (Or Segmentation), Statistical MT Systems, IBM Translation Models
  • Transcript: HTML PDF

 

Lecture 4    View Now >
1 hr 15 min

  • Topics: Review Statistical Mt, Model 1, The Em Algorithm, Em And Hidden Structure, Em Algorithm Demonstration In Excel Spreadsheet, Assignment 1
  • Transcript: HTML PDF

 

Lecture 5    View Now >
1 hr 10 min*

  • Topics: IBM Model 1-2 (Review), IBM Model 3, IBM Model 4, IBM Model 5, Mt Evaluation, Bleu Evaluation Metric, A Complete Translation System, Flaws Of Word-Based Mt, Phrased-Based Stat-Mt
  • Transcript: HTML PDF

 

Lecture 6    View Now >
1 hr 13 min*

  • Topics: Continue Of Machine Translation, Syntax-Based Model, Information Extraction & Named Entity Recognition, Information Extraction, Named Entity Extraction, Precision And Recall, Naive Bayes Classifiers
  • Transcript: HTML PDF

 

Lecture 7    View Now >
1 hr 15 min

  • Topics: Continue Of Naive Bayes Classifier, Joint V.S. Conditional Models, Features, Examples, Feature-Based Classifiers, Comparison To Naïve-Bayes, Building A Maxent Model
  • Transcript: HTML PDF

 

Lecture 8    View Now >
1 hr 16 min*

  • Topics: Details Of Maxent Model, Maxent Examples, Convexity, Feature Interaction, Classification, Smoothing, Inference In Systems
  • Transcript: HTML PDF

 

Lecture 9    View Now >
1 hr 7 min*

  • Topics: MEMM, Hmm Pos Tagging Model, Summary Of Tagging, NER, Information Extraction And Integration, Landscape Of IE Tasks, Machine Learning Methods, Relation Extraction, Clustering
  • Transcript: HTML PDF

 

Lecture 10    View Now >
1 hr 17 min

  • Topics: Parsing, Classical NLP Parsing, Two Views Of Linguistic Structure, Attachment Ambiguities, A Simple Prediction, What Is Parsing?, Top-Down Parsing, Bottom-Up Parsing, Parsing Of PCFGs
  • Transcript: HTML PDF

 

Lecture 11    View Now >
1 hr 17 min

  • Topics: Chomsky Normal Form, Cocke-Kasami-Younger (CKY) Constituency Parsing, Extended CKY Parsing, Efficient CKY Parsing, Evaluating Parsing Accuracy, How Good Are PCFGs?, Improve PCFG Parsing Via Unlexicalized Parsing, Markovization
  • Transcript: HTML PDF

 

Lecture 12    View Now >
1 hr 5 min

  • Topics: Guest Lecturer: Dan Jurafsky, Syntactic Variations Versus Semantic Roles, Some Typical Semantic Roles, Two Solutions To The Difficulty Of Defining Semantic Roles, PropBank, FrameNet, Information Extraction Versus Semantic Role Labeling, Evaluation Measures, Parsing Algorithm, Combining Identification And Classification Models, Summary
  • Transcript: HTML PDF

 

Lecture 13    View Now >
1 hr 16 min

  • Topics: Lexicalized Parsing, Parsing Via Classification Decisions: Charniak (1997), Sparseness & The Penn Treebank, Complexity Of Lexicalized PCFG Parsing, Complexity Of Lexicalized PCFG Parsing, Overview Of Collins’ Model, Choice Of Heads, The Latest Parsing Results, Parsing And Search Algorithms
  • Transcript: HTML PDF

 

Lecture 14    View Now >
1 hr 18 min

  • Topics: Parsing As Search, Agenda-Based Parsing, What Can Go Wrong?, Search In Modern Lexicalized Statistical Parsers, Dependency Parsing, Naïve Recognition/Parsing, Discriminative Parsing, Discriminative Models
  • Transcript: HTML PDF

 

Lecture 15    View Now >
1 hr 7 min*

  • Topics: Why Study Computational Semantics?, Precise Semantics. An Early Example: Chat-80, Programming Language Interpreter, Logic: Some Preliminaries, Compositional Semantics, A Simple DCG Grammar With Semantics, Augmented CFG Rules, Semantic Grammars
  • Transcript: HTML PDF

 

Lecture 16    View Now >
1 hr 15 min

  • Topics: An Introduction To Formal Computational Semantics, Database/ Knowledgebase Interfaces, Typed Lambda Calculus, Types Of Major Syntactic Categories, Adjective And PP Modification, Why Things Get More Complex, Generalized Quantifiers, Representing Proper Nouns With Quantifiers, Questions With Answers!, How Could We Learn Such Representations?
  • Transcript: HTML PDF

 

Lecture 17    View Now >
1 hr 12 min*

  • Topics: Lexical Semantics, Lexical Information And NL Applications, Polysemy Vs Homonymy, WordNet, Word Sense Disambiguation, Corpora Used For WSD Work, Evaluation, Lexical Acquisition, Vector-Based Lexical Semantics, Measures Of Semantic Similarity
  • Transcript: HTML PDF

 

Lecture 18    View Now >
1 hr 15 min*

  • Topics: Question Answering Systems And Textual Inference, A Brief (Academic) History, Top Performing Systems, Answer Types In State-Of-The-Art QA Systems, Semantics And Reasoning For QA, The Textual Inference Task, Why We Need Sloppy Matching, QA Beyond TREC
  • Transcript: HTML PDF