1. NLP

2. Natural Language Processing
Class Logistics

3. Quiz Where is this quote from?
Dave Bowman: Open the pod bay doors, HAL.
HAL: I’m sorry Dave. I’m afraid I can’t do that.
REPLACE

4. Quiz Answer “2001: A Space Odyssey” 1968 film by Stanley Kubrick
based on a joint screenplay with Arthur C. Clarke.

5. Watson Example

6. What is Natural Language Processing
Natural Language Processing (NLP) is the study of the computational treatment of natural (human) language.
In other words, teaching computers how to understand (and generate) human language.

7. How Computers Understand Language

8. Modern Applications Search engines Question answering
Google, Yahoo!, Bing, Baidu
Question answering
IBM’s Watson
Natural language assistants
Apple’s Siri
Translation systems
Google Translate
News digest
Yahoo!
Automated earthquake reports
LA Times
Automated stock market reports
Narrative Science

9. Notes Computers are confused by (human) language Goals of this class
Specific techniques are needed
NLP draws on research in Linguistics, Theoretical Computer Science, Mathematics, Statistics, Artificial Intelligence, Psychology, Databases, etc.
Goals of this class
Understand that language processing is hard (and why)
Understand the key problems in NLP
Learn about the methods used to address these problems
Understand the limitations of these methods

10. EECS 595/LING 541/SI 561 Instructor: Class times: GSI: Grader:
Dragomir Radev
Class times:
M 3:10-5:55 in 133 Chrysler
GSI:
Catherine Finegan-Dollak (cfdollak)
Grader:
TBA

11. EECS 595/LING 541/SI 561 Additional readings: Course home page:
Textbook:
Speech and Language Processing
by Jurafsky and Martin
Second edition, 2009
Additional readings:

12. Other Available Books
Foundations of Statistical Natural Language Processing
Chris Manning and Hinrich Schütze
Natural Language Understanding
James Allen
REFORMAT

13. Course Dates SEP OCT 5 NOV DEC no class Mon Oct 19
OCT 5
12 26
NOV
DEC
7 14
no class Mon Oct 19
midterm (unofficial) Nov 2
last class Mon Dec 14
exams Dec 16-23

14. Structure of the Course
Four major parts:
Linguistic, mathematical, and computational background
Computational models of morphology, syntax, semantics, discourse, pragmatics
Core NLP technology: parsing, part of speech tagging, text generation, semantic analysis, etc.
Applications: text classification, sentiment analysis, text summarization, question answering, machine translation, information extraction, etc.
Three major goals:
Learn the basic principles and theoretical issues underlying natural language processing
Learn techniques and tools used to develop practical, robust systems that can understand text and communicate with users in one or more languages
Gain insight into some open research problems in natural language
MOVE ELSEWHERE?

15. Syllabus Book sections Introduction (chapter 1) Words (chapters 2-6)
Syntax (chapters 12-16)
Semantics and Pragmatics (chapters 17-21)
Applications (chapters 22-25)

16. Draft Syllabus
Introduction Language Modeling Part-of-Speech Tagging Hidden Markov Models Formal Grammars of English Syntactic Parsing Statistical Parsing Features and Unification Dependency Parsing The Representation of Meaning Computational Semantics Lexical Semantics Computational Lexical Semantics Computational Discourse Information Extraction Question Answering and Summarization Dialog and Conversational Agents Machine Translation Sentiment and Subjectivity Analysis Vector Semantics Deep Learning for NLP

17. Grading Assignments 4 programming projects (60%) Midterm (15%)
Final (20%)
Class participation (5%)

18. Programming Projects Language Modeling and Part of Speech Tagging
Dependency Parsing
Vector Semantics for Word Sense Disambiguation
Machine Translation

19. More Sample Projects Noun phrase parser Paraphrase identification
Question answering
NL access to databases
Named entity tagging
Rhetorical parsing
Anaphora resolution
Document and sentence alignment
Using bioinformatics methods
Information extraction
Speech processing
Sentence normalization
Text summarization
Sentence compression
Definition extraction
Crossword puzzle generation
Prepositional phrase attachment
Machine translation
Generation
Semi-structured document parsing
Semantic analysis of short queries
User-friendly summarization
Number classification
Time-dependent fact extraction

20. Courses at Other Places
Brick-and-Mortar
Johns Hopkins University (Jason Eisner)
Cornell University (Lillian Lee)
Stanford University (Chris Manning, Dan Jurafsky, Richard Socher)
U. Maryland (Hal Daumé)
Berkeley (Dan Klein)
U. Texas (Ray Mooney)
Coursera
Manning/Jurafsky (2012, survey)
Michael Collins (2013, more advanced)

21. The Association for Computational Linguistics (ACL)

22. The Alphabet Soup NLP (Natural Language Processing)
CL (Computational Linguistics)
IR (Information Retrieval)
SP (Speech Processing)
HLT (Human Language Technology)
NLE (Natural Language Engineering)
ML (Machine Learning)

23. Research in NLP Conferences: Journals: University centers:
ACL/NAACL, EMNLP, SIGIR, AAAI/IJCAI, Coling, HLT, EACL/NAACL, AMTA/MT Summit, ICSLP/Eurospeech
Journals:
Computational Linguistics, TACL, Natural Language Engineering, Information Retrieval, Information Processing and Management, ACM Transactions on Information Systems, ACM TALIP, ACM TSLP
University centers:
Berkeley, Columbia, Stanford, CMU, JHU, Brown, UMass, MIT, UPenn, USC/ISI, Illinois, Michigan, UW, Maryland, etc.
Toronto, Edinburgh, Cambridge, Sheffield, Saarland, Trento, Prague, QCRI, NUS, and many others
Industrial research sites:
Google, MSR, Yahoo!, FB, IBM, SRI, BBN, MITRE, AT&T Labs
The ACL Anthology
The ACL Anthology Network (AAN)

24. NLP