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Torrent Details
Natural Language Processing
Category:
Other
Size:
1.1 GB
Seeders:
16
Leechers:
7
Downloads:
346
Uploaded:
Oct 21, 2018
Torrent
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Files
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Info
Natural Language Processing
1 - 1 - Course Introduction (14_11).mp4
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10 - 1 - What is Relation Extraction_ (9_47).mp4
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10 - 2 - Using Patterns to Extract Relations (6_17).mp4
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10 - 3 - Supervised Relation Extraction (10_51).mp4
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10 - 4 - Semi-Supervised and Unsupervised Relation Extraction (9_53).mp4
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11 - 1 - The Maximum Entropy Model Presentation (12_14).mp4
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11 - 2 - Feature Overlap_Feature Interaction (12_51).mp4
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11 - 3 - Conditional Maxent Models for Classification (4_11).mp4
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11 - 4 - Smoothing_Regularization_Priors for Maxent Models (29_24).mp4
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12 - 1 - An Intro to Parts of Speech and POS Tagging (13_19).mp4
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12 - 2 - Some Methods and Results on Sequence Models for POS Tagging (13_04).mp4
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13 - 1 - Syntactic Structure_ Constituency vs Dependency (8_46).mp4
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13 - 2 - Empirical_Data-Driven Approach to Parsing (7_11).mp4
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13 - 3 - The Exponential Problem in Parsing (14_30).mp4
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14 - 1 - Instructor Chat (9_02).mp4
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15 - 1 - CFGs and PCFGs (15_29).mp4
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15 - 2 - Grammar Transforms (12_05).mp4
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15 - 3 - CKY Parsing (23_25).mp4
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15 - 4 - CKY Example (21_52).mp4
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15 - 5 - Constituency Parser Evaluation (9_45).mp4
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16 - 1 - Lexicalization of PCFGs (7_03).mp4
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16 - 2 - Charniak_'s Model (18_23).mp4
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16 - 3 - PCFG Independence Assumptions (9_44).mp4
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16 - 4 - The Return of Unlexicalized PCFGs (20_53).mp4
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16 - 5 - Latent Variable PCFGs (12_07).mp4
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17 - 1 - Dependency Parsing Introduction (10_25).mp4
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17 - 2 - Greedy Transition-Based Parsing (31_05).mp4
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17 - 3 - Dependencies Encode Relational Structure (7_20).mp4
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18 - 1 - Introduction to Information Retrieval (9_16).mp4
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18 - 2 - Term-Document Incidence Matrices (8_59).mp4
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18 - 3 - The Inverted Index (10_42).mp4
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18 - 4 - Query Processing with the Inverted Index (6_43).mp4
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18 - 5 - Phrase Queries and Positional Indexes (19_45).mp4
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19 - 1 - Introducing Ranked Retrieval (4_27).mp4
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19 - 2 - Scoring with the Jaccard Coefficient (5_06).mp4
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19 - 3 - Term Frequency Weighting (5_59).mp4
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19 - 4 - Inverse Document Frequency Weighting (10_16).mp4
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19 - 5 - TF-IDF Weighting (3_42).mp4
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19 - 6 - The Vector Space Model (16_22).mp4
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19 - 7 - Calculating TF-IDF Cosine Scores (12_47).mp4
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19 - 8 - Evaluating Search Engines (9_02).mp4
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2 - 1 - Regular Expressions (11_25).mp4
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2 - 2 - Regular Expressions in Practical NLP (6_04).mp4
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2 - 3 - Word Tokenization (14_26).mp4
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2 - 4 - Word Normalization and Stemming (11_47).mp4
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2 - 5 - Sentence Segmentation (5_31).mp4
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20 - 1 - Word Senses and Word Relations (11_50).mp4
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20 - 2 - WordNet and Other Online Thesauri (6_23).mp4
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20 - 3 - Word Similarity and Thesaurus Methods (16_17).mp4
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20 - 4 - Word Similarity_ Distributional Similarity I (13_14).mp4
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20 - 5 - Word Similarity_ Distributional Similarity II (8_15).mp4
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21 - 1 - What is Question Answering_ (7_28).mp4
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21 - 2 - Answer Types and Query Formulation (8_47).mp4
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21 - 3 - Passage Retrieval and Answer Extraction (6_38).mp4
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21 - 4 - Using Knowledge in QA (4_25).mp4
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21 - 5 - Advanced_ Answering Complex Questions (4_52).mp4
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22 - 1 - Introduction to Summarization.mp4
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22 - 2 - Generating Snippets.mp4
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22 - 3 - Evaluating Summaries_ ROUGE.mp4
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22 - 4 - Summarizing Multiple Documents.mp4
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23 - 1 - Instructor Chat II (5_23).mp4
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3 - 1 - Defining Minimum Edit Distance (7_04).mp4
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3 - 2 - Computing Minimum Edit Distance (5_54).mp4
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3 - 3 - Backtrace for Computing Alignments (5_55).mp4
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3 - 4 - Weighted Minimum Edit Distance (2_47).mp4
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3 - 5 - Minimum Edit Distance in Computational Biology (9_29).mp4
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4 - 1 - Introduction to N-grams (8_41).mp4
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4 - 2 - Estimating N-gram Probabilities (9_38).mp4
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4 - 3 - Evaluation and Perplexity (11_09).mp4
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4 - 4 - Generalization and Zeros (5_15).mp4
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4 - 5 - Smoothing_ Add-One (6_30).mp4
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4 - 6 - Interpolation (10_25).mp4
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4 - 7 - Good-Turing Smoothing (15_35).mp4
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4 - 8 - Kneser-Ney Smoothing (8_59).mp4
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5 - 1 - The Spelling Correction Task (5_39).mp4
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5 - 2 - The Noisy Channel Model of Spelling (19_30).mp4
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5 - 3 - Real-Word Spelling Correction (9_19).mp4
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5 - 4 - State of the Art Systems (7_10).mp4
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6 - 1 - What is Text Classification_ (8_12).mp4
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6 - 2 - Naive Bayes (3_19).mp4
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6 - 3 - Formalizing the Naive Bayes Classifier (9_28).mp4
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6 - 4 - Naive Bayes_ Learning (5_22).mp4
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6 - 5 - Naive Bayes_ Relationship to Language Modeling (4_35).mp4
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6 - 6 - Multinomial Naive Bayes_ A Worked Example (8_58).mp4
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6 - 7 - Precision, Recall, and the F measure (16_16).mp4
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6 - 8 - Text Classification_ Evaluation (7_17).mp4
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6 - 9 - Practical Issues in Text Classification (5_56).mp4
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7 - 1 - What is Sentiment Analysis_ (7_17).mp4
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7 - 2 - Sentiment Analysis_ A baseline algorithm (13_27).mp4
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7 - 3 - Sentiment Lexicons (8_37).mp4
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7 - 4 - Learning Sentiment Lexicons (14_45).mp4
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7 - 5 - Other Sentiment Tasks (11_01).mp4
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8 - 1 - Generative vs. Discriminative Models (7_49).mp4
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8 - 2 - Making features from text for discriminative NLP models (18_11).mp4
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8 - 3 - Feature-Based Linear Classifiers (13_34).mp4
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8 - 4 - Building a Maxent Model_ The Nuts and Bolts (8_04).mp4
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8 - 5 - Generative vs. Discriminative models_ The problem of overcounting evidence (12_15).mp4
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8 - 6 - Maximizing the Likelihood (10_29).mp4
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9 - 1 - Introduction to Information Extraction (9_18).mp4
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9 - 2 - Evaluation of Named Entity Recognition (6_34).mp4
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9 - 3 - Sequence Models for Named Entity Recognition (15_05).mp4
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9 - 4 - Maximum Entropy Sequence Models (13_01).mp4
- 0 B
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InfoHash:
9AD3C282FF6C4137ED8B073D884EA3D72C2E4CD1
Last updated:
May 27, 2022
File count:
102 B
File Category:
1
Tags:
natural, language, processing
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