Syllabus Corpus Linguistics 24 Juli 2011.docx
PROGRAM STUDI BAHASA DAN SASTRA INGGRIS
JURUSAN PENDIDIKAN BAHASA INGGRIS
FAKULTAS PENDIDIKAN BAHASA DAN SENI
UNIVERSITAS PENDIDIKAN INDONESIA
Course
Code
Chs
Semester
Prerequisite
Lecturers
: Corpus Linguistics
: IG569
:4
:5
:: Dr. Dadang Sudana, MA
R. Dian D. Muniroh, S.Pd., M.Hum.
1. Objectives
Upon the completion of this course, students are expected to:
a) understand basic concepts of corpus linguistics, its history, as well as its
development,
b) be able to use programming language and its application in linguistics analysis,
and
c) get practical experiences of using computer to do some linguistic processing of a
corpus.
2. Course Description
This course introduces students to Corpus Linguistics, its history, as well as its
development. It also introduces students to the analysis of corpus, a large body of text
prepared for linguistic processing, using Python, an open source programming language
that comes with a sophisticated module for Natural Language Processing (NLP) called
Natural Language Toolkit (NLTK). The course provides students with practical
experiences of using computer in the analysis of a corpus.
3. Learning Activities
Lecturing, practical text analysis using computer, and discussions will be the major
modes for learning activities.
4. Media
Media are an LCD projector, a blackboard, computers, and corpus linguistics softwares.
5. Evaluation
Assessment will be based on the following aspects:
Class Participation : 15%
Research Project
: 30%
Presentation
: 30%
Final Test
: 25%
6. Course Outline
Sessions
Topics
Sources
1
Syllabus overview
Syllabus
2
Introduction to Corpus Linguistics
[1] [5] [4]
3
History of Corpus Linguistics
[4] [5]
4
Development of Corpus Linguistics
[4] [5]
5
Introduction to software of corpus processing
6
Language processing and Phyton
[2] [3]
7
Variables, Expressions and Statements
[2] [3]
8
Data Types in Python: String
[2] [3]
9
Data Types in Python: List
[2] [3]
Functions in Python
[2] [3]
10-11
[2]
12
Conditionals and Looping Python
[3]
13
Corpus in Natural Language Toolkit (NLTK)
[3]
14
NLTK's Corpus Functions
[2]
15
Processing NLTK's built-in corpus
[2]
16
Processing Raw Text from Local File
[2]
17-18
Projects’ discussions
19-26
Presentations
27
Final Test
7. References
[1] Baker, Paul, Andrew Hardie and Tony Mcenery. Glossary of Corpus Linguistics.
Edinburgh: Edinburgh University Press, 2006.
[2] Bird, Steven, Ewan Klein and Edward Loper. Natural Language Processing with
Python and NLTK. California: O'reilly Media, Inc, 2009.
[3] Downey, Allen B. Python for Software design. Cambridge: Cambridge University
Press, 2009.
[4] Meyer, Charles F. English Corpus Linguistics. Edinburgh: Edinburgh University
Press, 2006.
[5] Keffe, Anne and Michael McCarthy, eds. Routledge Handbook of Corpus
Linguistics. Oxford: Routledge, 2010
COURSE UNITS
Session
s
1
Topics
Specific Objectives
Learning Activities
Syllabus overview
Introduction to the subject:
The lecturer overview the
a) Welcoming remarks
syllabus
b) About the subject and its requirements
c) Overview of corpus linguistics
Introduction to
Corpus Linguistics
History of Corpus
Linguistics
Development of
Corpus Linguistics
Students are able to define what
corpus linguistics is
Students are able to mention the
history of corpus linguistics
Students are able to mention the
development of corpus linguistics
5
Introduction to
software of corpus
processing
Students are able to:
a) compare variety softwares for
corpus processing
b) identify the characteristics of
corpus softwares
6
Language processing
and Python
7
Variables,
Expressions and
2
3
4
Evaluation
Sources
Syllabus
The lecturer introduces
students to corpus linguistics
The lecturer explains the
history of corpus linguistics
The lecturer mentions the
development of corpus
linguistics
The lecturer introduces
students to software of corpus
processing
Question &
answer
Question &
answer
Question &
answer
[1] [5] [4]
Question &
answer
[2]
Students are able to identify text
processing by Python
The lecturer demonstrates text
processing using Python
Question &
answer
[2] [3]
Students are able to differentiate
between variables, expression, and
The lecturer explains variables,
expressions, and statements;
Question &
answer
[2] [3]
[4] [5]
[4] [5]
Statements
statements
8
Data Types in
Python: String
Students are able to specify data
types in Python particularly string
9
Data Types in
Python: List
Students are able to specify data
types in Python particularly list
Functions in Python
Students are able to define and use
the concepts of functions in Python
12
Conditionals and
Looping Python
Students are able to define and use
the concepts of conditionals and
looping Python
13
Corpus in Natural
Language Toolkit
(NLTK)
NLTK's Corpus
Functions
Students are able to define and use
the corpus in NLTK
Processing NLTK's
built-in corpora
Students are able to define and use
NLTK’s built-in corpora
10-11
14
15
Students are able to define and use
the NLTK’s Corpus Functions
Students practice using
variables, expressions, and
statements
The lecturer explains data
types in Python; Students
practice using the data types
(string)
The lecturer explains data
types in Python; Students
practice using the data types
(list)
The lecturer explains functions
in Pyhton; Students practice
using the functions in Python
The lecturer explains
conditionals and looping
Python; Students practice using
the functions in Python
The lecturer explains corpus in
NLTK; Students practice using
corpus in NLTK
The lecturer explains NLTK’s
corpus functions; Students
practice using NLTK’s corpus
functions
The lecturer explains
processing NLTK’s built-in
corpora; Students practice
using the processing NLTK’s
built-in corpora
Question &
answer
[2] [3]
Question &
answer
[2] [3]
Question &
answer
[2] [3]
Question &
answer
[3]
Question &
answer
[3]
Question &
answer
[2]
Question &
answer
[2]
16
Processing Raw
Text from Local File
Students are able to define and
process raw text from local file
17-18
Projects’ discussions
19-26
Presentations
Students are able to explain
problems they find during the
projects
Students are able to present their
research projects
27
Final Test
The lecturer explains
processing raw text from local
file; Students practice
processing raw text from local
file
Students discuss their research
projects with the lecturer
Question &
answer
Students present their research
projects
Question &
answer
Question &
answer
[2]
JURUSAN PENDIDIKAN BAHASA INGGRIS
FAKULTAS PENDIDIKAN BAHASA DAN SENI
UNIVERSITAS PENDIDIKAN INDONESIA
Course
Code
Chs
Semester
Prerequisite
Lecturers
: Corpus Linguistics
: IG569
:4
:5
:: Dr. Dadang Sudana, MA
R. Dian D. Muniroh, S.Pd., M.Hum.
1. Objectives
Upon the completion of this course, students are expected to:
a) understand basic concepts of corpus linguistics, its history, as well as its
development,
b) be able to use programming language and its application in linguistics analysis,
and
c) get practical experiences of using computer to do some linguistic processing of a
corpus.
2. Course Description
This course introduces students to Corpus Linguistics, its history, as well as its
development. It also introduces students to the analysis of corpus, a large body of text
prepared for linguistic processing, using Python, an open source programming language
that comes with a sophisticated module for Natural Language Processing (NLP) called
Natural Language Toolkit (NLTK). The course provides students with practical
experiences of using computer in the analysis of a corpus.
3. Learning Activities
Lecturing, practical text analysis using computer, and discussions will be the major
modes for learning activities.
4. Media
Media are an LCD projector, a blackboard, computers, and corpus linguistics softwares.
5. Evaluation
Assessment will be based on the following aspects:
Class Participation : 15%
Research Project
: 30%
Presentation
: 30%
Final Test
: 25%
6. Course Outline
Sessions
Topics
Sources
1
Syllabus overview
Syllabus
2
Introduction to Corpus Linguistics
[1] [5] [4]
3
History of Corpus Linguistics
[4] [5]
4
Development of Corpus Linguistics
[4] [5]
5
Introduction to software of corpus processing
6
Language processing and Phyton
[2] [3]
7
Variables, Expressions and Statements
[2] [3]
8
Data Types in Python: String
[2] [3]
9
Data Types in Python: List
[2] [3]
Functions in Python
[2] [3]
10-11
[2]
12
Conditionals and Looping Python
[3]
13
Corpus in Natural Language Toolkit (NLTK)
[3]
14
NLTK's Corpus Functions
[2]
15
Processing NLTK's built-in corpus
[2]
16
Processing Raw Text from Local File
[2]
17-18
Projects’ discussions
19-26
Presentations
27
Final Test
7. References
[1] Baker, Paul, Andrew Hardie and Tony Mcenery. Glossary of Corpus Linguistics.
Edinburgh: Edinburgh University Press, 2006.
[2] Bird, Steven, Ewan Klein and Edward Loper. Natural Language Processing with
Python and NLTK. California: O'reilly Media, Inc, 2009.
[3] Downey, Allen B. Python for Software design. Cambridge: Cambridge University
Press, 2009.
[4] Meyer, Charles F. English Corpus Linguistics. Edinburgh: Edinburgh University
Press, 2006.
[5] Keffe, Anne and Michael McCarthy, eds. Routledge Handbook of Corpus
Linguistics. Oxford: Routledge, 2010
COURSE UNITS
Session
s
1
Topics
Specific Objectives
Learning Activities
Syllabus overview
Introduction to the subject:
The lecturer overview the
a) Welcoming remarks
syllabus
b) About the subject and its requirements
c) Overview of corpus linguistics
Introduction to
Corpus Linguistics
History of Corpus
Linguistics
Development of
Corpus Linguistics
Students are able to define what
corpus linguistics is
Students are able to mention the
history of corpus linguistics
Students are able to mention the
development of corpus linguistics
5
Introduction to
software of corpus
processing
Students are able to:
a) compare variety softwares for
corpus processing
b) identify the characteristics of
corpus softwares
6
Language processing
and Python
7
Variables,
Expressions and
2
3
4
Evaluation
Sources
Syllabus
The lecturer introduces
students to corpus linguistics
The lecturer explains the
history of corpus linguistics
The lecturer mentions the
development of corpus
linguistics
The lecturer introduces
students to software of corpus
processing
Question &
answer
Question &
answer
Question &
answer
[1] [5] [4]
Question &
answer
[2]
Students are able to identify text
processing by Python
The lecturer demonstrates text
processing using Python
Question &
answer
[2] [3]
Students are able to differentiate
between variables, expression, and
The lecturer explains variables,
expressions, and statements;
Question &
answer
[2] [3]
[4] [5]
[4] [5]
Statements
statements
8
Data Types in
Python: String
Students are able to specify data
types in Python particularly string
9
Data Types in
Python: List
Students are able to specify data
types in Python particularly list
Functions in Python
Students are able to define and use
the concepts of functions in Python
12
Conditionals and
Looping Python
Students are able to define and use
the concepts of conditionals and
looping Python
13
Corpus in Natural
Language Toolkit
(NLTK)
NLTK's Corpus
Functions
Students are able to define and use
the corpus in NLTK
Processing NLTK's
built-in corpora
Students are able to define and use
NLTK’s built-in corpora
10-11
14
15
Students are able to define and use
the NLTK’s Corpus Functions
Students practice using
variables, expressions, and
statements
The lecturer explains data
types in Python; Students
practice using the data types
(string)
The lecturer explains data
types in Python; Students
practice using the data types
(list)
The lecturer explains functions
in Pyhton; Students practice
using the functions in Python
The lecturer explains
conditionals and looping
Python; Students practice using
the functions in Python
The lecturer explains corpus in
NLTK; Students practice using
corpus in NLTK
The lecturer explains NLTK’s
corpus functions; Students
practice using NLTK’s corpus
functions
The lecturer explains
processing NLTK’s built-in
corpora; Students practice
using the processing NLTK’s
built-in corpora
Question &
answer
[2] [3]
Question &
answer
[2] [3]
Question &
answer
[2] [3]
Question &
answer
[3]
Question &
answer
[3]
Question &
answer
[2]
Question &
answer
[2]
16
Processing Raw
Text from Local File
Students are able to define and
process raw text from local file
17-18
Projects’ discussions
19-26
Presentations
Students are able to explain
problems they find during the
projects
Students are able to present their
research projects
27
Final Test
The lecturer explains
processing raw text from local
file; Students practice
processing raw text from local
file
Students discuss their research
projects with the lecturer
Question &
answer
Students present their research
projects
Question &
answer
Question &
answer
[2]