LIN350 Analyzing Linguistic Data: Syllabus

Course: LIN 350 Analyzing Linguistic Data, unique number 39885

Semester: Spring 2023

Course Canvas page: https://utexas.instructure.com/courses/1359438

Place and time: Tuesday/Thursday 11-12:30, CBA 4.342. Directions to CBA: click here. 

Instructor:  Katrin Erk. office RLP 4.734, email: katrin.erk@utexas.edu
 Office hours: Monday 1-2 on zoom (see Canvas for link), Tuesday 1:30-3:30 in person ,RLP 4.734

Teaching Assistant:  Urvi Paresh Shah. Contact information on Canvas.
 Office hours: 3 PM - 4.30 PM on Tuesdays and 1.30 PM to 3 PM on Wednesdays. 

Prerequisites: Upper-division standing.

Textbook and readings: 

P. R. Hinton (2004): Statistics Explained: A Guide for Social Science Students. Psychology Press; 3rd edition, 2014

Additional required readings will be made available for download from the course website.

Flags: Quantitative Reasoning, Independent Inquiry 

Course overview and objectives


Today, huge amounts of text are available in electronic form. We can poke these electronic text collections to answer questions about language, and questions about the people who use it. For example, we can test whether passive constructions are increasingly falling out of favor in English, and we can trace how words change their meaning over time. We can also study a politician's word choices in political debates to find out more about their personality, or we can see how inaugural addresses have changed over time.

This course provides a hands-on introduction to working with text data. This includes an introduction to programming in Python, with a focus on text processing and data exploration, with a "cookbook" of programming examples that will enable you very quickly to analyze texts on your own. Most of the conclusions that we want to draw from text are "risky conclusions", they are trends rather than yes-or-no answers, so the course also includes an introduction to statistical techniques for data exploration and for making and assessing "risky conclusions". The course also includes a course project where you can test your text analysis skills on a question of your own choice.

By the end of this course, you will:

Quantitative Reasoning

This course carries the Quantitative Reasoning flag. Quantitative Reasoning courses are designed to equip you with skills that are necessary for understanding the types of quantitative arguments you will regularly encounter in your adult and professional life. You should therefore expect a substantial portion of your grade to come from your use of quantitative skills to analyze real-world problems.

Independent Inquiry

This course carries the Independent Inquiry flag. Independent Inquiry courses are designed to engage you in the process of inquiry over the course of a semester, providing you with the opportunity for independent investigation of a question, problem, or project related to your major. You should therefore expect a substantial portion of your grade to come from the independent investigation and presentation of your own work.

For more information on the project you will do in this course, see below under "Course project".

Course requirements and grading


Assignments: 48% (4 assigments, 12% each)
Assignments will be made available on Canvas. Tentative assignment due dates are marked in the schedule. The homework assignments will mostly be programming assignments appropriate to beginners, with a focus on text processing and statistics, and more theoretical exercises about statistics. Homeworks are designed to provide the foundation needed for course projects. 

"Food for thought" : 12% (4 mini-assigments, 3% each)
"Food for thought" assignments are smaller assignments that require you to think about larger questions such as the ethics of text processing, or that let you try out demos of text processing tools. 

Course project: 35%
You will turn in an initial project report and an intermediate report, each for 5% of your grade, and a final report, for 20% of the grade.  You will also do a project presentation for 5% of your grade. Tentative due dates are listed in the schedule. See below, under "Course Project", for more information on the course project. 

Course projects are typically done by teams of 2 students. Projects done by 1 or  3 students are only possible with prior approval of the instructor.

Project presentations will be in the final week of classes, in the order given on the schedule page (which will be generated via Python's random.shuffle()). If possible, all members of a project team should get some time to speak.

This course does not have a midterm or final exam.

Attendance: 5 % of grade

The course will use plus-minus grading, using the following scale:

A:  >= 93%.  

A-: >= 90%

B+: >= 87%. 

B: >= 83%. 

B-: >= 80%

C+: >= 77%. 

C: >= 73%. 

C-: >= 70%

D+: >= 67%. 

D: >= 63%.


Textbook

P. R. Hinton (2004): Statistics Explained: A Guide for Social Science Students. Psychology Press; 3rd edition, 2014

Additional readings will be made available for download from the course website.

Schedule


Assignments are due right before class (11am) on their due date unless noted otherwise. Assignment due dates are marked in red in the schedule.

Readings and course materials will be linked from the schedule below, from the date in question.

Unless otherwise noted, all readings can be done after class time.

This schedule is subject to change.

Week 1: Introduction

January 10:  Introduction. Some simple examples of text analysis.

January 12: Foundations of programming 

We'll be using Jupyter Notebooks in class. To use a  "Code for download" file, download it to your computer. Your computer will probably complain that it doesn't know how to open the file. This is not a problem, ignore it. Then either open it in Anaconda with notebooks, or open a terminal, go to the directory with the notebook, and type the command jupyter notebook

Week 2: Exploring and visualizing data

Jan 17: Exploring and visualizing data: the Inaugural Address collection

Jan 19: Exploring and visualizing data, continued

Week 3: Python basics

Jan 24: Python programming basics: conditions, lists, and loops

Jan 26: Python programming basics: conditions, lists, and loops, continued

Food for Thought 1 due

Week 4: Python basics

Jan 31:  no school, ice day

Feb 2: no school, ice day.

Homework 1 due: now Sunday end of day

Week 5: Python basics

Feb 7: We discuss your project ideas in class

Feb 9: Finishing up loops, then:
Python basics: dictionaries for word counting

Week 6: Python spreadsheets

Feb 14: Python dictionaries, continued

Feb 16: Making your own Python spreadsheets from word counts

Code for download: Merging Pandas data frames

Initial project description due

Week 7: Text processing

Feb 21: Tools for text processing: Splitting text into sentences and words, mapping words to their base form, filtering away stopwords, labeling words with their part of speech

Feb 23: Accessing texts in different writing systems, accessing texts from the web

Homework 2 due

Week 8: Identifying themes in text: tf/idf and clustering

Feb 28:  Identifying important words in a text: tf/idf and pointwise mutual information for computing word importance weights

Mar 2: Clustering to identify main themes in a text

Food for Thought 2 due

Week 9: Topic modeling

Mar 7: Clustering continued, and topic modeling

Mar 9: Topic modeling, continued

Week 10: Spring break

Week 11: Probabilities and hypothesis testing

    Mar 21: Descriptive statistics, probabilities and hypothesis testing

Mar 23: Hypothesis testing, and starting on the t-test

Week 12: Hypothesis testing, and more programming

Mar 28: Finishing up the t-test. Then:
Python list comprehensions, and how to use them with Pandas. 

Food for Thought 3 due

Mar 30: defining your own Python functions, and structuring your programs

Project progress report due

Week 13: Correlation and regression

April 4: Correlation

April 6: Linear regression

Homework 3 due

Week 14: Regression

April 11: Logistic regression

Food for Thought 4 due

April 13: Practicing regression

Week 15:

April 18: Project presentations:

11:00 Jillian Plant andBlake Griffin

11:15:  Evely Ludington, William Hartman, and Keziah Reina

11:30 Olivia Tucker

11:45 Gayoung Jeon

12:00 Isabel Erwin and Gabby Garcia

Homework 4 due

April 20: Project presentations:

11:00 Anna Alvis

11:15 Tran Nguyen and Erika Gonzalez

11:30 Doan Nguyen and Harini Shanmugam

11:45 Elizabeth Pena

Final paper due date: Thursday April 27, end of day

Attendance

It is crucial for your success in this class that you attend the lectures, do the in-class exercises and participate in in-class discussions. The TA will keep an attendance sheet. Please remember to enter your name into the attendance sheet each time you come to class. You can have three missed class sessions without penalty. For each missed class sessions beyond three, your attendance grade will decrease by 4 out of 100 points. Exceptions to this rule (due to medical emergencies, etc.) are at the discretion of your teacher. An important rule of thumb for an extension-related conversation is be communicative, be proactive, and let us know ahead of time. 

Extension policy

If you turn in your assignment late and we have not agreed on an extension beforehand, expect points to be deducted. Extensions will be considered on a case-by-case basis. I urge you to let me know if you are in need of an extension, such that we can make sure that you get the time necessary to complete the assignments.

If an extension has not been agreed on beforehand, then for assignments, by default, 5 points (out of 100) will be deducted for lateness, plus an additional 1 point for every 24-hour period beyond 2 that the assignment is late.

Note that there are always some points to be had, even if you turn in your assignment late. So if you would like to know if you should still turn in the assignment even though it is late, the answer is yes. The last class day in the semester (April 24, 2023) is the last day to turn in late assignments for grading.

Academic honesty

Students who violate University rules on scholastic dishonesty are subject to disciplinary penalties, including the possibility of failure in the course and/or dismissal from the University. Since such dishonesty harms the individual, all students and the integrity of the University, policies on scholastic dishonesty will be strictly enforced. For further information, please visit the Office of Student Conduct and Academic Integrity website at http://deanofstudents.utexas.edu/conduct/.


Notice about students with disabilities

The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. Please contact the Division of Diversity and Community Engagement, Services for Students with Disabilities, 471-6259.

Notice about missed work due to religious holy days

A student who misses an examination, work assignment, or other project due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.

Emergency Evacuation Policy

Occupants of buildings on The University of Texas at Austin campus are required to evacuate buildings when a fire alarm is activated. Alarm activation or announcement requires exiting and assembling outside. Familiarize yourself with all exit doors of each classroom and building you may occupy. Remember that the nearest exit door may not be the one you used when entering the building. Students requiring assistance in evacuation shall inform their instructor in writing during the first week of class. In the event of an evacuation, follow the instruction of faculty or class instructors. Do not re-enter a building unless given instructions by the following: Austin Fire Department, The University of Texas at Austin Police Department, or Fire Prevention Services office. Information regarding emergency evacuation routes and emergency procedures can be found at http://www.utexas.edu/emergency.

Behavior Concerns Advice Line (BCAL)

If you are worried about someone who is acting differently, you may use the Behavior Concerns Advice Line to discuss by phone your concerns about another individual's behavior. This service is provided through a partnership among the Office of the Dean of Students, the Counseling and Mental Health Center (CMHC), the Employee Assistance Program (EAP), and The University of Texas Police Department (UTPD). Call 512-232-5050 or visit http://www.utexas.edu/safety/bcal

Senate Bill 212 and Title IX Reporting Requirements

Under Senate Bill 212 (SB 212), the professor and TAs for this course are required to report for further investigation any information concerning incidents of sexual harassment, sexual assault, dating violence, and stalking committed by or against a UT student or employee. Federal law and university policy also requires reporting incidents of sex- and gender-based discrimination and sexual misconduct (collectively known as Title IX incidents). This means we cannot keep confidential information about any such incidents that you share with us. If you need to talk with someone who can maintain confidentiality, please contact University Health Services (512-471-4955 or 512-475-6877) or the UT Counseling and Mental Health Center (512-471-3515 or 512-471-2255). We strongly urge you make use of these services for any needed support and that you report any Title IX incidents to the Title IX Office.

Use of E-mail for Official Correspondence to Students

All students should be familiar with the University’s official e-mail student notification policy. It is the student’s responsibility to keep the University informed as to changes in his or her e-mail address. Students are expected to check e-mail on a frequent and regular basis in order to stay current with University-related communications, recognizing that certain communications may be time-critical. The complete text of this policy and instructions for updating your e-mail address are available at http://www.utexas.edu/its/policies/emailnotify.html .

Course project

Course project requirements:

In addition, each team member submits a short (half page) document describing their individual contribution and reflecting on what they learned in the project so far.

You will need to prepare slides for this, which you submit to the instructor ahead of time.

It is okay if you don't have all results in place at this point. This does not lead to points being taken away for the presentation.

If you build on previous work, you need to discuss it, and give references.
Published papers (at conferences, in journals) go into the references list at the end of the paper. Links to blog posts and the like go in a footnote. Also, links to websites containing data go in a footnote, not in the references list.

You need to take into account the feedback that you got on the Initial project description and Intermediate report.

In addition, each team member submits a short (half page) document describing their individual contribution and reflecting on what they learned in the project.

Course project ideas

Ideally, you pick a topic of your own that you are curious about. But to give you an idea of possible topics, here are a few pointers:

Please discuss your topic with the instructor to make sure that it is both substantial and feasible.

For your course project, you will need to apply statistical analyses yourself. Google books n-gram charts, while pretty, do not count.

Useful links

List of software we will use in the class

Python and Python packages:

To test your Python installation, use this Jupyter notebook.

Using Jupyter notebooks

Jupyter notebooks that we use in class are listed on the class schedule page. Click on a Jupyter notebook link there to download the file. To access the notebook:

For info on how to format text and write code in Jupyter notebooks, see this Jupyter notebook.

Learning Python

Learning Python:

General Python pages:

The Natural Language Toolkit:

Fun with statistics