إنشاء حساب
الرئيسية
الكورسات
Software Development & Programming
Natural Language Processing Full Course – University of Michigan
Lecture 41 — Word Sense Disambiguation - Natural Language Processing | Michigan

Lecture 41 — Word Sense Disambiguation - Natural Language Processing | Michigan

Natural Language Processing Full Course – University of Michigan

الدرس السابق الدرس التالي

محتوى الكورس

جميع الدروس
81 درس
08:39 Lecture 1 — Introduction - Natural Language Processing | University of Michigan
07:52 Lecture 2 — Examples of Text - Natural Language Processing | University of Michigan
06:33 Lecture 3 — Funny Sentences - Natural Language Processing | University of Michigan
08:07 Lecture 4 — Administrative - Natural Language Processing | University of Michigan
25:56 Lecture 5 — Why is NLP hard - Natural Language Processing | University of Michigan
16:55 Lecture 6 — Background - Natural Language Processing | University of Michigan
21:28 Lecture 7 — Linguistics - Natural Language Processing | University of Michigan
07:27 Lecture 8 — Text Similarity (Introduction) - Natural Language Processing | Michigan
15:50 Lecture 9 — Parts of Speech - Natural Language Processing | University of Michigan
20:22 Lecture 10 — Morphology and the Lexicon - Natural Language Processing | Michigan
14:55 Lecture 11 — Morphological Similarity (Stemming) - Natural Language Processing
20:54 Lecture 12 — Spelling Similarity (Edit Distance) - Natural Language Processing
03:47 Lecture 13 — NACLO - Natural Language Processing
11:31 Lecture 14 — Preprocessing - Natural Language Processing
14:17 Lecture 15 — Semantic Similarity- Synonymy and other Semantic Relations - NLP
07:39 Lecture 16 — Thesaurus-based Word Similarity Methods - Natural Language Processing
09:21 Lecture 17 — The Vector Space Model - Natural Language Processing | Michigan
23:10 Lecture 18 — Dimensionality Reduction - Natural Language Processing | Michigan
14:33 Lecture 19 — NLP Tasks 1-3 - Natural Language Processing | University of Michigan
15:22 Lecture 20 — NLP Tasks 2-3 - Natural Language Processing | University of Michigan
16:22 Lecture 21 — NLP Tasks 3-3 - Natural Language Processing | University of Michigan
31:03 Lecture 22 — Syntax - Natural Language Processing | University of Michigan
17:13 Lecture 23 — Parsing - Natural Language Processing | University of Michigan
25:03 Lecture 24 — Classic Parsing Methods - Natural Language Processing | Michigan
16:25 Lecture 25 — Earley Parser - Natural Language Processing | University of Michigan
17:40 Lecture 26 — The Penn Treebank - Natural Language Processing | University of Michigan
15:17 Lecture 27 — Parsing Introduction and recap Parsing noun sequences - NLP
14:43 Lecture 28 — Prepositional phrase attachment (1/3) -Natural Language Processing | Michigan
16:45 Lecture 29 — Prepositional phrase attachment (2/3) - Natural Language Processing | Michigan
12:35 Lecture 30 — Prepositional phrase attachment (3/3) | Natural Language Processing | Michigan
12:45 Lecture 31 — Statistical Parsing - Natural Language Processing | University of Michigan
09:00 Lecture 32 — Lexicalized Parsing - Natural Language Processing | University of Michigan
18:28 Lecture 33 — Dependency Parsing - Natural Language Processing | University of Michigan
10:00 Lecture 34 — Alternative Parsing Formalisms - Natural Language Processing | Michigan
21:12 Lecture 35 — Probabilities - Natural Language Processing | University of Michigan
10:49 Lecture 36 — Bayes Theorem - Natural Language Processing | University of Michigan
19:22 Lecture 37 — Language Modeling (1/3) - Natural Language Processing | Michigan
03:06 Lecture 38 — Language Modeling (1/3 cont 'd ) - Natural Language Processing | Michigan
10:57 Lecture 39 — Language Modeling (2/3) - Natural Language Processing | Michigan
15:07 Lecture 40 — Language Modeling (3/3)- Natural Language Processing | Michigan
20:07 Lecture 41 — Word Sense Disambiguation - Natural Language Processing | Michigan
08:34 Lecture 42 — Noisy Channel Model - Natural Language Processing | University of Michigan
17:58 Lecture 43 — Part of Speech Tagging - Natural Language Processing | Michigan
24:42 Lecture 44 — Hidden Markov Models (1/2) - Natural Language Processing | Michigan
05:29 Lecture 45 — Hidden Markov Models (2/2) - Natural Language Processing | Michigan
09:21 Lecture 46 — Statistical POS Tagging - Natural Language Processing | Michigan
05:35 Lecture 47 — Information Extraction - Natural Language Processing | Michigan
21:12 Lecture 48 — Relation Extraction - Natural Language Processing | University of Michigan
21:21 Lecture 49 — Question Answering - Natural Language Processing | University of Michigan
21:41 Lecture 50 — Evaluation of QA , System Architecture | NLP| University of Michigan
07:57 Lecture 1 — Introduction - Natural Language Processing | University of Michigan
14:08 Lecture 2 — Examples of Text - Natural Language Processing | University of Michigan
10:40 Lecture 3 — Funny Sentences - Natural Language Processing | University of Michigan
11:38 Lecture 4 — Administrative - Natural Language Processing | University of Michigan
19:22 Lecture 5 — Why is NLP hard - Natural Language Processing | University of Michigan
20:13 Lecture 6 — Background - Natural Language Processing | University of Michigan
10:11 Lecture 7 — Linguistics - Natural Language Processing | University of Michigan
25:19 Lecture 8 — Text Similarity (Introduction) - Natural Language Processing | Michigan
05:20 Lecture 9 — Parts of Speech - Natural Language Processing | University of Michigan
11:34 Lecture 10 — Morphology and the Lexicon - Natural Language Processing | Michigan
21:11 Lecture 11 — Morphological Similarity (Stemming) - Natural Language Processing
11:10 Lecture 12 — Spelling Similarity (Edit Distance) - Natural Language Processing
26:08 Lecture 13 — NACLO - Natural Language Processing
15:20 Lecture 14 — Preprocessing - Natural Language Processing
02:24 Lecture 15 — Semantic Similarity- Synonymy and other Semantic Relations - NLP
08:44 Lecture 16 — Thesaurus-based Word Similarity Methods - Natural Language Processing
07:48 Lecture 17 — The Vector Space Model - Natural Language Processing | Michigan
06:56 Lecture 18 — Dimensionality Reduction - Natural Language Processing | Michigan
09:17 Lecture 19 — NLP Tasks 1-3 - Natural Language Processing | University of Michigan
07:32 Lecture 20 — NLP Tasks 2-3 - Natural Language Processing | University of Michigan
12:01 Lecture 21 — NLP Tasks 3-3 - Natural Language Processing | University of Michigan
06:37 Lecture 22 — Syntax - Natural Language Processing | University of Michigan
09:18 Lecture 23 — Parsing - Natural Language Processing | University of Michigan
14:57 Lecture 24 — Classic Parsing Methods - Natural Language Processing | Michigan
16:40 Lecture 25 — Earley Parser - Natural Language Processing | University of Michigan
09:23 Lecture 26 — The Penn Treebank - Natural Language Processing | University of Michigan
10:56 Lecture 27 — Parsing Introduction and recap Parsing noun sequences - NLP
11:49 Lecture 28 — Prepositional phrase attachment (1/3) -Natural Language Processing | Michigan
11:54 Lecture 29 — Prepositional phrase attachment (2/3) - Natural Language Processing | Michigan
09:37 Lecture 30 — Prepositional phrase attachment (3/3) | Natural Language Processing | Michigan
05:50 Lecture 31 — Statistical Parsing - Natural Language Processing | University of Michigan