Natural Language Processing

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Natural Language Processing

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种子哈希：9ad3c282ff6c4137ed8b073d884ea3d72c2e4cd1
文件大小： 1.1G
已经下载：23890次
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收录时间：2017-02-08
最近下载：2025-01-03

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文件列表

  • 1 - 1 - Course Introduction (14_11).mp4 12.9 MB
  • 10 - 1 - What is Relation Extraction_ (9_47).mp4 10.7 MB
  • 10 - 2 - Using Patterns to Extract Relations (6_17).mp4 6.4 MB
  • 10 - 3 - Supervised Relation Extraction (10_51).mp4 10.8 MB
  • 10 - 4 - Semi-Supervised and Unsupervised Relation Extraction (9_53).mp4 10.6 MB
  • 11 - 1 - The Maximum Entropy Model Presentation (12_14).mp4 18.1 MB
  • 11 - 2 - Feature Overlap_Feature Interaction (12_51).mp4 13.2 MB
  • 11 - 3 - Conditional Maxent Models for Classification (4_11).mp4 5.0 MB
  • 11 - 4 - Smoothing_Regularization_Priors for Maxent Models (29_24).mp4 30.2 MB
  • 12 - 1 - An Intro to Parts of Speech and POS Tagging (13_19).mp4 12.5 MB
  • 12 - 2 - Some Methods and Results on Sequence Models for POS Tagging (13_04).mp4 13.4 MB
  • 13 - 1 - Syntactic Structure_ Constituency vs Dependency (8_46).mp4 9.4 MB
  • 13 - 2 - Empirical_Data-Driven Approach to Parsing (7_11).mp4 7.6 MB
  • 13 - 3 - The Exponential Problem in Parsing (14_30).mp4 15.6 MB
  • 14 - 1 - Instructor Chat (9_02).mp4 24.9 MB
  • 15 - 1 - CFGs and PCFGs (15_29).mp4 17.5 MB
  • 15 - 2 - Grammar Transforms (12_05).mp4 12.6 MB
  • 15 - 3 - CKY Parsing (23_25).mp4 27.5 MB
  • 15 - 4 - CKY Example (21_52).mp4 24.6 MB
  • 15 - 5 - Constituency Parser Evaluation (9_45).mp4 11.2 MB
  • 16 - 1 - Lexicalization of PCFGs (7_03).mp4 7.5 MB
  • 16 - 2 - Charniak_'s Model (18_23).mp4 19.9 MB
  • 16 - 3 - PCFG Independence Assumptions (9_44).mp4 10.3 MB
  • 16 - 4 - The Return of Unlexicalized PCFGs (20_53).mp4 22.2 MB
  • 16 - 5 - Latent Variable PCFGs (12_07).mp4 13.2 MB
  • 17 - 1 - Dependency Parsing Introduction (10_25).mp4 11.7 MB
  • 17 - 2 - Greedy Transition-Based Parsing (31_05).mp4 32.9 MB
  • 17 - 3 - Dependencies Encode Relational Structure (7_20).mp4 7.6 MB
  • 18 - 1 - Introduction to Information Retrieval (9_16).mp4 9.5 MB
  • 18 - 2 - Term-Document Incidence Matrices (8_59).mp4 9.5 MB
  • 18 - 3 - The Inverted Index (10_42).mp4 11.2 MB
  • 18 - 4 - Query Processing with the Inverted Index (6_43).mp4 7.1 MB
  • 18 - 5 - Phrase Queries and Positional Indexes (19_45).mp4 21.6 MB
  • 19 - 1 - Introducing Ranked Retrieval (4_27).mp4 4.8 MB
  • 19 - 2 - Scoring with the Jaccard Coefficient (5_06).mp4 5.7 MB
  • 19 - 3 - Term Frequency Weighting (5_59).mp4 6.7 MB
  • 19 - 4 - Inverse Document Frequency Weighting (10_16).mp4 11.7 MB
  • 19 - 5 - TF-IDF Weighting (3_42).mp4 4.3 MB
  • 19 - 6 - The Vector Space Model (16_22).mp4 17.8 MB
  • 19 - 7 - Calculating TF-IDF Cosine Scores (12_47).mp4 13.9 MB
  • 19 - 8 - Evaluating Search Engines (9_02).mp4 9.2 MB
  • 2 - 1 - Regular Expressions (11_25).mp4 11.4 MB
  • 2 - 2 - Regular Expressions in Practical NLP (6_04).mp4 8.4 MB
  • 2 - 3 - Word Tokenization (14_26).mp4 13.1 MB
  • 2 - 4 - Word Normalization and Stemming (11_47).mp4 10.6 MB
  • 2 - 5 - Sentence Segmentation (5_31).mp4 5.2 MB
  • 20 - 1 - Word Senses and Word Relations (11_50).mp4 15.6 MB
  • 20 - 2 - WordNet and Other Online Thesauri (6_23).mp4 9.2 MB
  • 20 - 3 - Word Similarity and Thesaurus Methods (16_17).mp4 21.2 MB
  • 20 - 4 - Word Similarity_ Distributional Similarity I (13_14).mp4 15.8 MB
  • 20 - 5 - Word Similarity_ Distributional Similarity II (8_15).mp4 9.9 MB
  • 21 - 1 - What is Question Answering_ (7_28).mp4 9.3 MB
  • 21 - 2 - Answer Types and Query Formulation (8_47).mp4 10.6 MB
  • 21 - 3 - Passage Retrieval and Answer Extraction (6_38).mp4 8.1 MB
  • 21 - 4 - Using Knowledge in QA (4_25).mp4 5.5 MB
  • 21 - 5 - Advanced_ Answering Complex Questions (4_52).mp4 6.5 MB
  • 22 - 1 - Introduction to Summarization.mp4 6.3 MB
  • 22 - 2 - Generating Snippets.mp4 10.1 MB
  • 22 - 3 - Evaluating Summaries_ ROUGE.mp4 6.8 MB
  • 22 - 4 - Summarizing Multiple Documents.mp4 14.1 MB
  • 23 - 1 - Instructor Chat II (5_23).mp4 19.5 MB
  • 3 - 1 - Defining Minimum Edit Distance (7_04).mp4 6.9 MB
  • 3 - 2 - Computing Minimum Edit Distance (5_54).mp4 5.6 MB
  • 3 - 3 - Backtrace for Computing Alignments (5_55).mp4 5.8 MB
  • 3 - 4 - Weighted Minimum Edit Distance (2_47).mp4 3.0 MB
  • 3 - 5 - Minimum Edit Distance in Computational Biology (9_29).mp4 9.4 MB
  • 4 - 1 - Introduction to N-grams (8_41).mp4 8.0 MB
  • 4 - 2 - Estimating N-gram Probabilities (9_38).mp4 9.9 MB
  • 4 - 3 - Evaluation and Perplexity (11_09).mp4 10.1 MB
  • 4 - 4 - Generalization and Zeros (5_15).mp4 4.9 MB
  • 4 - 5 - Smoothing_ Add-One (6_30).mp4 6.3 MB
  • 4 - 6 - Interpolation (10_25).mp4 9.8 MB
  • 4 - 7 - Good-Turing Smoothing (15_35).mp4 14.1 MB
  • 4 - 8 - Kneser-Ney Smoothing (8_59).mp4 8.8 MB
  • 5 - 1 - The Spelling Correction Task (5_39).mp4 5.1 MB
  • 5 - 2 - The Noisy Channel Model of Spelling (19_30).mp4 18.7 MB
  • 5 - 3 - Real-Word Spelling Correction (9_19).mp4 9.0 MB
  • 5 - 4 - State of the Art Systems (7_10).mp4 6.9 MB
  • 6 - 1 - What is Text Classification_ (8_12).mp4 8.1 MB
  • 6 - 2 - Naive Bayes (3_19).mp4 3.4 MB
  • 6 - 3 - Formalizing the Naive Bayes Classifier (9_28).mp4 8.6 MB
  • 6 - 4 - Naive Bayes_ Learning (5_22).mp4 6.5 MB
  • 6 - 5 - Naive Bayes_ Relationship to Language Modeling (4_35).mp4 4.3 MB
  • 6 - 6 - Multinomial Naive Bayes_ A Worked Example (8_58).mp4 11.9 MB
  • 6 - 7 - Precision, Recall, and the F measure (16_16).mp4 16.5 MB
  • 6 - 8 - Text Classification_ Evaluation (7_17).mp4 12.1 MB
  • 6 - 9 - Practical Issues in Text Classification (5_56).mp4 6.9 MB
  • 7 - 1 - What is Sentiment Analysis_ (7_17).mp4 10.0 MB
  • 7 - 2 - Sentiment Analysis_ A baseline algorithm (13_27).mp4 13.8 MB
  • 7 - 3 - Sentiment Lexicons (8_37).mp4 11.1 MB
  • 7 - 4 - Learning Sentiment Lexicons (14_45).mp4 19.6 MB
  • 7 - 5 - Other Sentiment Tasks (11_01).mp4 15.2 MB
  • 8 - 1 - Generative vs. Discriminative Models (7_49).mp4 8.3 MB
  • 8 - 2 - Making features from text for discriminative NLP models (18_11).mp4 17.5 MB
  • 8 - 3 - Feature-Based Linear Classifiers (13_34).mp4 14.1 MB
  • 8 - 4 - Building a Maxent Model_ The Nuts and Bolts (8_04).mp4 8.2 MB
  • 8 - 5 - Generative vs. Discriminative models_ The problem of overcounting evidence (12_15).mp4 12.8 MB
  • 8 - 6 - Maximizing the Likelihood (10_29).mp4 10.3 MB
  • 9 - 1 - Introduction to Information Extraction (9_18).mp4 9.8 MB
  • 9 - 2 - Evaluation of Named Entity Recognition (6_34).mp4 7.1 MB
  • 9 - 3 - Sequence Models for Named Entity Recognition (15_05).mp4 14.8 MB
  • 9 - 4 - Maximum Entropy Sequence Models (13_01).mp4 13.9 MB

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