Introduction to Natural Language Processing

About this course: This course provides an introduction to the field of Natural Language Processing. It includes relevant background material in Linguistics, Mathematics, Probabilities, and Computer Science. Some of the topics covered in the class are Text Similarity, Part of Speech Tagging, Parsing, Semantics, Question Answering, Sentiment Analysis, and Text Summarization. The course includes quizzes, programming assignments in Python, and a final exam. Course Syllabus Week One (Introduction 1/2) (1:35:31) Week Two (Introduction 2/2) (1:36:26) Week Three (NLP Tasks and Text Similarity) (1:42:52) Week Four (Syntax and Parsing, Part 1) (1:48:14) Week Five (Syntax and Parsing, Part 2) (1:50:29) Week Six (Language Modeling and Word Sense Disambiguation) (1:40:33) Week Seven (Part of Speech Tagging and Information Extraction) (1:33:21) Week Eight (Question Answering) (1:16:59) Week Nine (Text Summarization) (1:33:55) Week Ten (Collocations and Information Retrieval) (1:29:40) Week Eleven (Sentiment Analysis and Semantics) (1:09:38) Week Twelve (Discourse, Machine Translation, and Generation) (1:30:57) The course assignments will all be in Python. Course Format The class will consist of lecture videos, which are typically between 10 and 25 minutes in length. The lectures contain 1-2 integrated quiz questions per video. Grading is based on three programming assignments, weekly quizzes, and a final exam.

Who is this class for: This class is for students interested in Computational Linguistics and Natural Language Processing. Some previous experience with probabilities will be helpful. Prior or concurrent experience with programming, preferably in Python, is expected.

Created by:  University of Michigan

• Taught by:  Dragomir R. Radev, Ph.D., Professor of Information, School of Information, Professor of Electrical Engineering and Computer Science, College of Engineering, and Professor of Linguistics, College of Literature, Science, and the Arts

  College of Engineering, School of Information, School of Literature, Science and the Arts

Level Intermediate Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.1 stars Average User Rating 4.1See what learners said Coursework

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Syllabus


WEEK 1


Week One: Introduction 1/2
In Week One, you will be watching an introductory lecture that covers the motivation for NLP, examples of difficult cases, as well as the first part of the Introduction to Linguistics needed for this class.


7 videos, 5 readings expand

1. Reading: Course Details
2. Reading: Help us learn more about you!
3. Reading: Credits
4. Reading: Preview: Python dependencies for HW1
5. Reading: Miscellaneous Notes
6. Video: 01.01 - Introduction (8:38)
7. Video: 01.02 - Examples of Text (7:51)
8. Video: 01.03 - Funny Sentences (6:32) - optional
9. Video: 01.04 - Administrative (8:06)
10. Video: 01.05 - Why is NLP hard? (25:55)
11. Video: 01.06 - Background (16:54)
12. Video: 01.07 - Linguistics (21:27)

Graded: Quiz 1

WEEK 2


Week Two: Introduction 2/2



Week Two will cover Parts of Speech, Morphology, Text Similarity, and Text Preprocessing. I will also introduce NACLO, the North American Computational Linguistics Olympiad (www.nacloweb.org), a competition for high school students interested in NLP and Linguistics.


7 videos, 1 reading expand

1. Reading: Welcome to Week Two
2. Video: 02.01 - Parts of speech (15:49)
3. Video: 02.02 - Morphology and the Lexicon (20:20)
4. Video: 02.03 - Text Similarity: Introduction (7:26)
5. Video: 02.04 - Morphological Similarity: Stemming (14:54)
6. Video: 02.05 - Spelling Similarity: Edit Distance (20:53)
7. Video: 02.06 - NACLO (3:46)
8. Video: 02.07 - Preprocessing (11:30)

Graded: Quiz 2 (note that this quiz refers to some material taught in Week Three)

WEEK 3


Week Three: NLP Tasks and Text Similarity
Week Three will cover Vector Semantics, Text Similarity, and Dimensionality Reduction. I will also go through a long list of sample NLP tasks (e.g., Information Extraction, Text Summarization, and Semantic Role Labeling) and introduce each of them briefly.


7 videos, 1 reading expand

1. Reading: Welcome to Week Three
2. Video: 03.01 - Semantic Similarity: Synonymy and other Semantic Relations (14:16)
3. Video: 03.02 - Thesaurus-based Word Similarity Methods (7:38)
4. Video: 03.03 - The Vector Space Model (9:20)
5. Video: 03.04 - Dimensionality Reduction (23:09)
6. Video: 03.05 - NLP Tasks 1/3 (14:32)
7. Video: 03.06 - NLP Tasks 2/3 (15:21)
8. Video: 03.07 - NLP Tasks 3/3 (16:21)

Graded: Quiz 3

WEEK 4


Week Four: Syntax and Parsing, Part 1
Week Four will cover the basics of Syntax and Parsing, including CKY parsing and the Earley parser.


5 videos, 1 reading expand

1. Reading: Welcome to Week Four
2. Video: 04.01 - Syntax (31:02)
3. Video: 04.02 - Parsing (17:12)
4. Video: 04.03 - Classic Parsing Methods (25:02)
5. Video: 04.04 - Earley Parser (16:24)
6. Video: 04.05 - The Penn Treebank (17:39)

Graded: Quiz 4

WEEK 5


Week Five: Syntax and Parsing, Part 2
Week Five will continue with topics related to parsing, including Statistical, Lexicalized, and Dependency Parsing as well as Noun Sequence Parsing, Prepositional Phrase Attachment, and Alternative Grammatical Formalisms.


8 videos, 1 reading expand

1. Reading: Welcome to Week Five
2. Video: 05.01 - Parsing Introduction and recap/Parsing noun sequences (15:16)
3. Video: 05.02 - Prepositional phrase attachment 1/3 (14:42) - optional
4. Video: 05.03 - Prepositional phrase attachment 2/3 (16:44) - optional
5. Video: 05.04 - Prepositional phrase attachment 3/3 (12:34) - optional
6. Video: 05.05 - Statistical Parsing (12:44)
7. Video: 05.06 - Lexicalized Parsing (8:59)
8. Video: 05.07 - Dependency Parsing (18:57)
9. Video: 05.08 - Alternative Parsing Formalisms (9:58)

Graded: Quiz 5
Graded: Dependency Parsing - you can start this assignment now

WEEK 6


Week Six: Language Modeling
Week Six will cover Probabilities, Language Modeling, and Word Sense Disambiguation (WSD). The first two, along with some material coming up in Week Seven, will be the basis for Assignment 2. The WSD unit will be needed later for Assignment 3.


7 videos, 1 reading expand

1. Reading: Welcome to Week Six
2. Video: 06.01 - Probabilities (21:11)
3. Video: 06.02 - Bayes Theorem (10:48)
4. Video: 06.03 - Language Modeling 1/3 (19:21)
5. Video: 06.04 - Language Modeling 1/3 (cont'd) (3:05)
6. Video: 06.05 - Language Modeling 2/3 (10:56)
7. Video: 06.06 - Language Modeling 3/3 (15:06)
8. Video: 06.07 - Word Sense Disambiguation (20:06)

Graded: Quiz 6
Graded: Language Modeling and Part of Speech Tagging - you can start this assignment now

WEEK 7


Week Seven: Part of Speech Tagging and Information Extraction
Week Seven includes the Noisy Channel Model, Hidden Markov Models, Part of Speech Tagging (all needed for the second programming assignment) and a short introduction to Information Extraction.


7 videos, 1 reading expand

1. Reading: Welcome to Week Seven
2. Video: 07.01 - Noisy Channel Model (8:33)
3. Video: 07.02 - Part of Speech Tagging (17:57)
4. Video: 07.03 - Hidden Markov Models 1/2 (24:41)
5. Video: 07.04 - Hidden Markov Models 2/2 (5:28)
6. Video: 07.05 - Statistical POS Tagging (9:20)
7. Video: 07.06 - Information Extraction (5:34)
8. Video: 07.07 - Relation Extraction (21:11)

Graded: Quiz 7
Graded: Word Sense Disambiguation - you can start this assignment now

WEEK 8


Week Eight: Question Answering
Week Eight covers different topics related to Question Answering, including Question Type Classification and Evaluation of Question Answering Systems.


5 videos, 1 reading expand

1. Reading: Welcome to Week Eight
2. Video: 08.01 - Question Answering (21:20)
3. Video: 08.02 - Evaluation of QA , System Architecture (21:40)
4. Video: 08.03 - QA System Architecture (7:56)
5. Video: 08.04 - Question Answering Systems 1/2 (14:07)
6. Video: 08.05 - Question Answering Systems 2/2 (10:39)

Graded: Quiz 8

WEEK 9


Week Nine: Text Summarization
Week Nine covers Text Summarization and related topics such as Sentence Compression.


6 videos, 1 reading expand

1. Reading: Welcome to Week Nine
2. Video: 09.01 - Summarization (11:37)
3. Video: 09.02 - Summarization Techniques 1/3 (19:21)
4. Video: 09.03 - Summarization Techniques 2/3 (20:12)
5. Video: 09.04 - Summarization Techniques 3/3 (10:10)
6. Video: 09.05 - Summarization Evaluation (25:18)
7. Video: 09.06 - Sentence Simplification (5:18) - optional

Graded: Quiz 9

WEEK 10


Week Ten: Collocations and Information Retrieval
Week Ten covers Information Retrieval (including Document Indexing, Ranking, Evaluation), Text Classification and Text Clustering, as well as a short lecture on Collocations.


6 videos, 1 reading expand

1. Reading: Welcome to Week Ten
2. Video: 10.01 - Collocations (11:33) - optional
3. Video: 10.02 - Information Retrieval (21:10)
4. Video: 10.03 - Evaluation of IR (11:09)
5. Video: 10.04 - Text Classification (26:07)
6. Video: 10.05 - Text Clustering (15:19)
7. Video: 10.06 - Information Retrieval Toolkits (2:23)

Graded: Quiz 10

WEEK 11


Week Eleven: Sentiment Analysis and Semantics
Week Eleven covers Semantics and related topics such as Sentiment Analysis, Semantic Parsing, and Knowledge Representation.


8 videos, 1 reading expand

1. Reading: Welcome to Week Eleven
2. Video: 11.01 - Sentiment Analysis (8:43)
3. Video: 11.02 - Sentiment Lexicons (7:47)
4. Video: 11.03 - Semantics (6:55)
5. Video: 11.04 - Representing and Understanding Meaning (9:16)
6. Video: 11.05 - First Order Logic (7:31)
7. Video: 11.06 - Knowledge Representation (12:00)
8. Video: 11.07 - Inference (6:36)
9. Video: 11.08 - Semantic Parsing (9:17)

WEEK 12


Week Twelve: Discourse, Machine Translation, and Generation (Includes Final Exam)
Week Twelve briefly covers Discourse Analysis, Dialogue, Machine Translation, and Text Generation.


8 videos, 2 readings expand

1. Reading: Welcome to Week Twelve
2. Video: 12.01 - Discourse Analysis (14:56)
3. Video: 12.02 - Coherence (16:39)
4. Video: 12.03 - Dialogue Systems (9:22) - optional
5. Video: 12.04 - Machine Translation (10:55)
6. Video: 12.05 - Machine Translation Basic Techniques (11:48)
7. Video: 12.06 - Machine Translation Noisy Channel Methods (11:53)
8. Video: 12.07 - Machine Translation Advanced Methods (9:36)
9. Video: 12.08 - Text Generation (5:49) - optional
10. Reading: Post-course Survey

Graded: Final Exam