N.L.P.
NATURAL LANGUAGE PROCESSING
Teacher: Lê Ngọc Tấn
Email: letan.dhcn@gmail.com
Blog: http://lengoctan.wordpress.com
Trường Đại học Công nghiệp Tp. HCM
Khoa Công nghệ thông tin
(Faculty of Information Technology)
NLP. p.2
CONTENT
Chapter 1. Introduction and Overview of NLP
Chapter 2. Fundamental algorithms and mathematical models
Chapter 3. Basic principles for NLP
Chapter 4. Computational Linguistics
Chapter 5. Foundation of Statistical Machine Translation
C.1 –Introduction and Overview of NLP
NLP. p.3
Chapter 1
Introduction and Overview of NLP
C.1 –Introduction and Overview of NLP
Introduce some of the classical problems in NLP
Learn to address empirical problems
–Is one system for a task better than another
–Understand where and how a system fails
–Propose possible solutions
Talk/write clearly about your work, decision and observations
NLP. p.4
In NLP module, we will
C.1 –Introduction and Overview of NLP
No background in NLP is required
Expect to know a bit of basic probability (know Bayes rules)
Know a bit about vectors and vector space, a bit of calculus
(matrices)
Have reasonable programming ability (know about hash tables
and graph data structures, Java, Python, Perl, Prolog,…)
NLP. p.5
What background do I need?
C.1 –Introduction and Overview of NLP