d8:announce41:https://academictorrents.com/announce.php13:announce-listll41:https://academictorrents.com/announce.phpel46:https://ipv6.academictorrents.com/announce.phpel42:udp://tracker.opentrackr.org:1337/announceel44:udp://tracker.openbittorrent.com:80/announceel43:udp://tracker.coppersurfer.tk:6969/announceel30:udp://9.rarbg.to:2920/announceel33:udp://tracker.opentrackr.org:1337el48:udp://tracker.internetwarriors.net:1337/announceel49:udp://tracker.leechers-paradise.org:6969/announceel42:udp://tracker.pirateparty.gr:6969/announceel38:udp://tracker.cyberia.is:6969/announceee4:infod5:filesld6:lengthi12851778e4:pathl39:1 - 1 - Course Introduction (14_11).mp4eed6:lengthi10680110e4:pathl48:10 - 1 - What is Relation Extraction_ (9_47).mp4eed6:lengthi6371205e4:pathl55:10 - 2 - Using Patterns to Extract Relations (6_17).mp4eed6:lengthi10807085e4:pathl51:10 - 3 - Supervised Relation Extraction (10_51).mp4eed6:lengthi10551783e4:pathl72:10 - 4 - Semi-Supervised and Unsupervised Relation Extraction (9_53).mp4eed6:lengthi18121462e4:pathl59:11 - 1 - The Maximum Entropy Model Presentation (12_14).mp4eed6:lengthi13245126e4:pathl56:11 - 2 - Feature Overlap_Feature Interaction (12_51).mp4eed6:lengthi5024866e4:pathl64:11 - 3 - Conditional Maxent Models for Classification (4_11).mp4eed6:lengthi30194480e4:pathl70:11 - 4 - Smoothing_Regularization_Priors for Maxent Models (29_24).mp4eed6:lengthi12458870e4:pathl64:12 - 1 - An Intro to Parts of Speech and POS Tagging (13_19).mp4eed6:lengthi13440042e4:pathl80:12 - 2 - Some Methods and Results on Sequence Models for POS Tagging (13_04).mp4eed6:lengthi9396889e4:pathl67:13 - 1 - Syntactic Structure_ Constituency vs Dependency (8_46).mp4eed6:lengthi7592114e4:pathl61:13 - 2 - Empirical_Data-Driven Approach to Parsing (7_11).mp4eed6:lengthi15591142e4:pathl55:13 - 3 - The Exponential Problem in Parsing (14_30).mp4eed6:lengthi24929898e4:pathl35:14 - 1 - Instructor Chat (9_02).mp4eed6:lengthi17454177e4:pathl35:15 - 1 - CFGs and PCFGs (15_29).mp4eed6:lengthi12630649e4:pathl39:15 - 2 - Grammar Transforms (12_05).mp4eed6:lengthi27454495e4:pathl32:15 - 3 - CKY Parsing (23_25).mp4eed6:lengthi24578429e4:pathl32:15 - 4 - CKY Example (21_52).mp4eed6:lengthi11178479e4:pathl50:15 - 5 - Constituency Parser Evaluation (9_45).mp4eed6:lengthi7470717e4:pathl43:16 - 1 - Lexicalization of PCFGs (7_03).mp4eed6:lengthi19879718e4:pathl38:16 - 2 - Charniak_'s Model (18_23).mp4eed6:lengthi10312492e4:pathl49:16 - 3 - PCFG Independence Assumptions (9_44).mp4eed6:lengthi22247035e4:pathl54:16 - 4 - The Return of Unlexicalized PCFGs (20_53).mp4eed6:lengthi13162688e4:pathl42:16 - 5 - Latent Variable PCFGs (12_07).mp4eed6:lengthi11693342e4:pathl52:17 - 1 - Dependency Parsing Introduction (10_25).mp4eed6:lengthi32884912e4:pathl52:17 - 2 - Greedy Transition-Based Parsing (31_05).mp4eed6:lengthi7590902e4:pathl60:17 - 3 - Dependencies Encode Relational Structure (7_20).mp4eed6:lengthi9500464e4:pathl57:18 - 1 - Introduction to Information Retrieval (9_16).mp4eed6:lengthi9459108e4:pathl52:18 - 2 - Term-Document Incidence Matrices (8_59).mp4eed6:lengthi11230487e4:pathl39:18 - 3 - The Inverted Index (10_42).mp4eed6:lengthi7066067e4:pathl60:18 - 4 - Query Processing with the Inverted Index (6_43).mp4eed6:lengthi21599783e4:pathl58:18 - 5 - Phrase Queries and Positional Indexes (19_45).mp4eed6:lengthi4799841e4:pathl48:19 - 1 - Introducing Ranked Retrieval (4_27).mp4eed6:lengthi5656452e4:pathl56:19 - 2 - Scoring with the Jaccard Coefficient (5_06).mp4eed6:lengthi6668251e4:pathl44:19 - 3 - Term Frequency Weighting (5_59).mp4eed6:lengthi11655516e4:pathl57:19 - 4 - Inverse Document Frequency Weighting (10_16).mp4eed6:lengthi4295495e4:pathl36:19 - 5 - TF-IDF Weighting (3_42).mp4eed6:lengthi17757334e4:pathl43:19 - 6 - The Vector Space Model (16_22).mp4eed6:lengthi13877671e4:pathl53:19 - 7 - Calculating TF-IDF Cosine Scores (12_47).mp4eed6:lengthi9243706e4:pathl45:19 - 8 - Evaluating Search Engines (9_02).mp4eed6:lengthi11373353e4:pathl39:2 - 1 - Regular Expressions (11_25).mp4eed6:lengthi8350021e4:pathl55:2 - 2 - Regular Expressions in Practical NLP (6_04).mp4eed6:lengthi13074814e4:pathl37:2 - 3 - Word Tokenization (14_26).mp4eed6:lengthi10570476e4:pathl51:2 - 4 - Word Normalization and Stemming (11_47).mp4eed6:lengthi5214068e4:pathl40:2 - 5 - Sentence Segmentation (5_31).mp4eed6:lengthi15615929e4:pathl51:20 - 1 - Word Senses and Word Relations (11_50).mp4eed6:lengthi9171716e4:pathl53:20 - 2 - WordNet and Other Online Thesauri (6_23).mp4eed6:lengthi21218615e4:pathl58:20 - 3 - Word Similarity and Thesaurus Methods (16_17).mp4eed6:lengthi15756476e4:pathl65:20 - 4 - Word Similarity_ Distributional Similarity I (13_14).mp4eed6:lengthi9919516e4:pathl65:20 - 5 - Word Similarity_ Distributional Similarity II (8_15).mp4eed6:lengthi9324839e4:pathl47:21 - 1 - What is Question Answering_ (7_28).mp4eed6:lengthi10614733e4:pathl54:21 - 2 - Answer Types and Query Formulation (8_47).mp4eed6:lengthi8051671e4:pathl59:21 - 3 - Passage Retrieval and Answer Extraction (6_38).mp4eed6:lengthi5529644e4:pathl41:21 - 4 - Using Knowledge in QA (4_25).mp4eed6:lengthi6469067e4:pathl57:21 - 5 - Advanced_ Answering Complex Questions (4_52).mp4eed6:lengthi6313091e4:pathl42:22 - 1 - Introduction to Summarization.mp4eed6:lengthi10078009e4:pathl32:22 - 2 - Generating Snippets.mp4eed6:lengthi6848699e4:pathl40:22 - 3 - Evaluating Summaries_ ROUGE.mp4eed6:lengthi14052542e4:pathl43:22 - 4 - Summarizing Multiple Documents.mp4eed6:lengthi19533921e4:pathl38:23 - 1 - Instructor Chat II (5_23).mp4eed6:lengthi6919423e4:pathl49:3 - 1 - Defining Minimum Edit Distance (7_04).mp4eed6:lengthi5645956e4:pathl50:3 - 2 - Computing Minimum Edit Distance (5_54).mp4eed6:lengthi5797467e4:pathl53:3 - 3 - Backtrace for Computing Alignments (5_55).mp4eed6:lengthi2969122e4:pathl49:3 - 4 - Weighted Minimum Edit Distance (2_47).mp4eed6:lengthi9384844e4:pathl65:3 - 5 - Minimum Edit Distance in Computational Biology (9_29).mp4eed6:lengthi8007030e4:pathl42:4 - 1 - Introduction to N-grams (8_41).mp4eed6:lengthi9941704e4:pathl50:4 - 2 - Estimating N-gram Probabilities (9_38).mp4eed6:lengthi10064342e4:pathl45:4 - 3 - Evaluation and Perplexity (11_09).mp4eed6:lengthi4896742e4:pathl43:4 - 4 - Generalization and Zeros (5_15).mp4eed6:lengthi6335739e4:pathl37:4 - 5 - Smoothing_ Add-One (6_30).mp4eed6:lengthi9834414e4:pathl33:4 - 6 - Interpolation (10_25).mp4eed6:lengthi14090863e4:pathl41:4 - 7 - Good-Turing Smoothing (15_35).mp4eed6:lengthi8847896e4:pathl39:4 - 8 - Kneser-Ney Smoothing (8_59).mp4eed6:lengthi5077371e4:pathl47:5 - 1 - The Spelling Correction Task (5_39).mp4eed6:lengthi18650250e4:pathl55:5 - 2 - The Noisy Channel Model of Spelling (19_30).mp4eed6:lengthi8980029e4:pathl48:5 - 3 - Real-Word Spelling Correction (9_19).mp4eed6:lengthi6928793e4:pathl43:5 - 4 - State of the Art Systems (7_10).mp4eed6:lengthi8076326e4:pathl47:6 - 1 - What is Text Classification_ (8_12).mp4eed6:lengthi3407247e4:pathl30:6 - 2 - Naive Bayes (3_19).mp4eed6:lengthi8584874e4:pathl57:6 - 3 - Formalizing the Naive Bayes Classifier (9_28).mp4eed6:lengthi6485101e4:pathl40:6 - 4 - Naive Bayes_ Learning (5_22).mp4eed6:lengthi4285727e4:pathl65:6 - 5 - Naive Bayes_ Relationship to Language Modeling (4_35).mp4eed6:lengthi11936700e4:pathl60:6 - 6 - Multinomial Naive Bayes_ A Worked Example (8_58).mp4eed6:lengthi16483983e4:pathl56:6 - 7 - Precision, Recall, and the F measure (16_16).mp4eed6:lengthi12105475e4:pathl50:6 - 8 - Text Classification_ Evaluation (7_17).mp4eed6:lengthi6882321e4:pathl58:6 - 9 - Practical Issues in Text Classification (5_56).mp4eed6:lengthi10020633e4:pathl46:7 - 1 - What is Sentiment Analysis_ (7_17).mp4eed6:lengthi13824348e4:pathl60:7 - 2 - Sentiment Analysis_ A baseline algorithm (13_27).mp4eed6:lengthi11089818e4:pathl37:7 - 3 - Sentiment Lexicons (8_37).mp4eed6:lengthi19557652e4:pathl47:7 - 4 - Learning Sentiment Lexicons (14_45).mp4eed6:lengthi15231164e4:pathl41:7 - 5 - Other Sentiment Tasks (11_01).mp4eed6:lengthi8308899e4:pathl55:8 - 1 - Generative vs. Discriminative Models (7_49).mp4eed6:lengthi17474220e4:pathl75:8 - 2 - Making features from text for discriminative NLP models (18_11).mp4eed6:lengthi14114584e4:pathl52:8 - 3 - Feature-Based Linear Classifiers (13_34).mp4eed6:lengthi8174462e4:pathl62:8 - 4 - Building a Maxent Model_ The Nuts and Bolts (8_04).mp4eed6:lengthi12812676e4:pathl94:8 - 5 - Generative vs. Discriminative models_ The problem of overcounting evidence (12_15).mp4eed6:lengthi10308780e4:pathl45:8 - 6 - Maximizing the Likelihood (10_29).mp4eed6:lengthi9845898e4:pathl57:9 - 1 - Introduction to Information Extraction (9_18).mp4eed6:lengthi7083001e4:pathl57:9 - 2 - Evaluation of Named Entity Recognition (6_34).mp4eed6:lengthi14834619e4:pathl64:9 - 3 - Sequence Models for Named Entity Recognition (15_05).mp4eed6:lengthi13945790e4:pathl51:9 - 4 - Maximum Entropy Sequence Models (13_01).mp4eee4:name27:Natural Language Processing12:piece lengthi1048576e6:pieces22460:0E)߸7F?䟫ø8E+oXcKOFj*Ew_FCP2^>K77Xv;M85yz?OV z;J> L 3,/dVM=9Rmvw{! 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