Linguistics 2195 Python Programming for Language Researchers Course Home (Spring 2019)


Table of Contents:


 

Instructor: Chongwon Park, Ph.D. (TA: Trevor Winger)

Google group address: ling2195_002s19d@d.umn.edu

Office Hours: 10:00 - 10:55AM, MW (H420S)

Class Time: 3:00 - 3:50PM, MWF (Sports and Health Center 208)

Required Texts:

The Quick Python Book -- 3rd Edition (Naomi Ceder, Manning)
Natural Language Processing with Python (Steven Bird, Ewan Klein, and Edward Loper, O'Reilly)

Course Description:

The aim of this course is to learn computer programming using Python within the context of linguistic analysis. Students will learn basic concepts of programming. After learning how to create basic code using the said concepts, students will create more domain specific programs to clean up and reformat a large set of text data. The learning outcomes of this course are: [1] Designining short programs (scripts) that incorporate basic concepts in Python programming, such as variables, types, expressions, etc.; [2] Understanding the data structures of Python; [3] Performing morpho-syntactic analyses of English sentences using NLTK or other packages; [4] Evaluating the efficiency and/or accuracy of different types of linguistic analysis tools such as NLTK parser and Stanford parser; [5] Creating a program that generates syntactic analyses of a selected text from the Gutenberg Project or COCA.

Requirements:

You will have a total of 10 assignments and 3 exams. All assignments and exams consist of problem-solving questions.

Attendance and Evaluation:

It is important for you to be present for every class. Every homework assignment should be turned in on the due date (or before the due date) in class. Evaluation will be based on the following weight. IMPORTANT: I DO NOT accept late assignments (no exceptions). E-mail submissions WILL NOT be accepted.

Number
Points
Total
Homework
10
5 (per homework)
50
Exam 1
1
10
10
Exam 2
1
15
15
Exam 3
1
25
25
Total
100

While students are expected to attend every single class period, there are circumstances that lead to excused absence from the classroom. Excused absences are defined at http://www.duluth.umn.edu/vcaa/ExcusedAbsence.html. To be eligible for an excused absence, students must provide written documents such as doctor's notes and advisor's letters. To encourage your attendance, for each class you miss 1 point will be deducted, but if your attendance is perfect (any absences being excused) you will receive 3 bonus points.

Final Grades:

Course Schedule for Spring 2019:

Date
Topic
Assignments and Due Dates
Required Reading
Jan. 16 (W)
Introduction
Jan. 18 (F)
Basics
Ceder, Ch. 4
Jan. 21 (M)
No Class
Jan. 23 (W)
List, tuples, and sets
Ceder, Ch. 5
Jan. 25 (F)
Strings
Assignment, Due Feb. 1 (F)
Ceder, Ch. 6
Jan. 28 (M)
Dictionaries
Ceder, Ch. 7
Jan. 30 (W)
Contron flow
Ceder, Ch. 8
Feb. 1 (F)
Control flow
Assignment 2, Due Feb. 8 (F)
Ceder, Ch. 8
Feb. 4 (M)
Functions
Ceder, Ch. 9
Feb. 6 (W)
Functions
Ceder, Ch. 9
Feb. 8 (F)
Functions
Assignment 3, Due Feb. 15 (F)
Ceder, Ch. 9
Feb. 11 (M)
Modules and scoping rules
Ceder, Ch. 10
Feb. 13 (W)
Reading and writing files
Ceder, Ch. 13
Feb. 15 (F)
Exceptions
Assignment 4, Due Feb. 22 (F)
Ceder, Ch. 14
Feb. 18 (M)
Classes and OOP
Ceder, Ch. 15
Feb. 20 (W)
Classes and OOP
Ceder, Ch. 15
Feb. 22 (F)
Regular Expressions
Ceder, Ch. 16
Feb. 25 (M)
Regular Expressions
Assignment 5, Due Mar. 4 (M)
Ceder, Ch. 16
Feb. 27 (W)
Basic file wrangling
Ceder, Ch. 20
Mar. 1 (F)
Processing data files
Ceder, Ch. 21
Mar. 4 (M)
Processing data files
Ceder, Ch. 21
Mar. 6 (W)
Exam 1
Ceder, Ch. 1 ~ Ch. 16
Mar. 8 (F)
Data over network
Assignment 6, Due Mar. 18 (M)
Ceder, Ch. 22
Mar. 11 (M)
No Class
Mar. 13 (W)
No Class
Mar. 15 (F)
No Class
Mar. 18 (M)
Data over network
Ceder, Ch. 22
Mar. 20 (W)
Saving Data
Ceder, Ch. 23
Mar. 22 (F)
Saving Data
Assignment 7, Due Mar. 29 (F)
Ceder, Ch. 23
Mar. 25 (M)
Processing raw text
Bird et al., Ch. 3
Mar. 27 (W)
Processing raw text
Bird et al., Ch. 3
Mar. 29 (F)
Processing raw text
Assignment 8, Due Apr. 5 (F)
Bird et al., Ch. 3
Apr. 1 (M)
Categorizing and tagging words
Bird et al., Ch. 5
Apr. 3 (W)
Categorizing and tagging words
Bird et al., Ch. 5
Apr. 5 (F)
Categorizing and tagging words
Assignment 9, Due Apr. 12 (F)
Bird et al., Ch. 5
Apr. 8 (M)
Analyzing sentence structures
Bird et al. Ch. 8
Apr. 10 (W)
Analyzing sentence structures
Bird et al., Ch. 8
Apr. 12 (F)
Exam 2
Bird et al., Ch 3 and Ch 5
Apr. 15 (M)
Analyzing sentence structures
Bird et al., Ch. 8
Apr. 17 (W)
No Class
Apr. 19 (F)
No Class
Apr. 22 (M)
No class
Apr. 24 (W)
Building feature-based grammars
Assignment 10, Due May 1 (W)
Bird et al., Ch. 9
Apr. 26 (F)
Building feature-based grammars
Bird et al., Ch. 9
Apr. 29 (M)
Building feature-based grammars
Bird et al., Ch. 9
May 1 (W)
Analyzing the meaning of sentences
Bird et al., Ch. 10
May 3 (F)
Analyzing the meaning of sentences
Bird et al., Ch. 10
 
Final Exam (Take-home)
Due May 10, 2019 (4PM)
Comprehensive

 

Academic Dishonesty:

Academic dishonesty tarnishes UMD's reputation and discredits the accomplishments of students. UMD is committed to providing students every possible opportunity to grow in mind and spirit. This pledge can only be redeemed in an environment of trust, honesty, and fairness. As a result, academic dishonesty is regarded as a serious offense by all members of the academic community. In keeping with this ideal, this course will adhere to UMD's Student Academic Integrity Policy, which can be found at http://www.d.umn.edu/conduct/integrity. This policy sanctions students engaging in academic dishonesty with penalties up to and including expulsion from the university for repeat offenders.

Appropriate Classroom Conduct:

The instructor will enforce and students are expected to follow the University's Student Conduct Code (http://www.d.umn.edu/conduct/code). Appropriate classroom conduct promotes an environment of academic achievement and integrity. Disruptive classroom behavior that substantially or repeatedly interrupts either the instructor's ability to teach, or student learning, is prohibited. Disruptive behavior includes inappropriate use of technology in the classroom. Examples include ringing cell phones, text-messaging, watching videos, playing computer games, checking email, surfing the Internet, or Facebooking (or facebooking) on your computer instead of note-taking or other instructor-sanctioned activities.