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Natural Language Processing

Instructor(s):

Dates:

March 06, 2015 to April 03, 2015 July 17, 2015 to August 14, 2015 March 04, 2016 to April 01, 2016 July 15, 2016 to August 12, 2016 March 03, 2017 to March 31, 2017 July 14, 2017 to August 11, 2017 March 02, 2018 to March 30, 2018 July 13, 2018 to August 10, 2018

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Natural Language Processing

taught by Nitin Indurkhya

Aim of Course:

This course is designed to give you an introduction to the algorithms, techniques and software used in natural language processing (NLP). Their use will be illustrated by reference to existing applications, particularly speech understanding, information retrieval, machine translation and information extraction. The course will try to make clear both the capabilities and the limitations of these applications.

For real-world applications, NLP draws heavily on work in computational linguistics and artificial intelligence. The course textbook will provide the necessary background in linguistics and computer science for those students who need it. In this course only a portion of the textbook will be covered, however anyone going on to do further studies in NLP will find the textbook a very useful reference.

At the completion of the course, a student should be able to read the description of an NLP application and have an idea of how it is done, what the likely weaknesses are, and often which claims are probably exaggerated. The course also prepares students to do further work in NLP by giving them a good grasp of the basic concepts.

This course may be taken individually (one-off) or as part of a certificate program.

Course Program:

WEEK 1: Introduction of NLP and Word-level Analysis

  • Overview of NLP
  • Text Preprocessing
  • Corpus Creation
  • Fundamental Statistical Techniques in NLP (review)
  • Lexical Analysis

WEEK 2: Sentence-level Processing

  • Part-of-Speech Tagging
  • Context-Free Grammars (CFG)
  • Parsing of sentences with CFG
  • Statistical parsing methods

WEEK 3: Semantics

  • Representation of Meaning
  • Semantic Analysis
  • Word Sense Disambiguation

WEEK 4: Applications of NLP

  • Information Retrieval
  • Information Extraction
  • Speech Recognition Systems
  • Natural Language Generation


HOMEWORK:

Homework in this course consists of short answer questions to test concepts.

In addition to assigned readings, this course also has supplemental readings available online.

Natural Language Processing

Instructor(s):

Dates:
March 06, 2015 to April 03, 2015 July 17, 2015 to August 14, 2015 March 04, 2016 to April 01, 2016 July 15, 2016 to August 12, 2016 March 03, 2017 to March 31, 2017 July 14, 2017 to August 11, 2017 March 02, 2018 to March 30, 2018 July 13, 2018 to August 10, 2018

Course Fee: $549

Do you meet course prerequisites? What about book & software? (Click here to learn more)

Tuition Savings:  When you register online for 3 or more courses, $200 is automatically deducted from the total tuition. (This offer cannot be combined and is only applicable to courses of 3 weeks or longer.)

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Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

Natural Language Processing

taught by Nitin Indurkhya

Who Should Take This Course:

Analysts, researchers and managers who deal with, or might need to deal with, NLP systems at a variety of levels - needs assessment, design, deployment and operation.

Level:

Intermediate

Prerequisite:
These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.
Students should be familiar with probability (e.g. the material covered in Statistics 1 and Statistics 2). Some familiarity with Bayesian statistics (such as that covered in Introduction to Bayesian Statistics) is also helpful, although the text does cover the required Bayesian fundamentals to a limited degree. Keep in mind that this course is an introductory/survey course with a broad brush approach, and, as such, does not get into computational intensity on a comprehensive basis.
Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement: about 15 hours per week, at times of  your choosing.


Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

The course text is Handbook of Natural Language Processing (2nd edition), by Nitin Indurkhya and Fred Damerau, which you can order online, or by using this form. CRC Press typically gives students a generous discount when students order the text using the above form (not by ordering the text online).

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Software:

None.


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