CSCI 544 – Applied Natural Language Processing

D-clearance and waiting list

The course CSCI 544 — Applied Natural Language Processing has been oversubscribed in recent semesters, and therefore the department has instituted a prioritized waiting list for receiving D-clearance to enroll in the course.

CS Master’s students

D-clearance for CS Master’s students is handled by the department office. Please do not write to us asking to enroll in the course: we have no more information than the department office on which to base a decision, and therefore we will defer to the department’s priority list.

Exception: If you are a CS Master’s student working on a research project with a faculty advisor, and your advisor thinks that taking this class is needed for the project, please have your faculty advisor write to us to ask for D-clearance.

Other students

Undergraduate students, PhD students, and students from other departments who wish to register for the course should write to us. We will decide whether to give you D-clearance based on your academic background and interests.

Our general policy is to ask the department to give priority to Ph.D. students, and to place Master’s and undergraduate students on the priority list subject to the same criteria as CS Master’s students. If you are a Master’s or undergraduate student working on a research project with a faculty advisor, and your advisor thinks that taking this class is needed for the project, please have your faculty advisor write to us to ask for higher priority on the list.

Late registrations (during the add period)

As students register for the class, they are automatically added to Blackboard; this usually happens within a few hours after enrolling in the class. If you are enrolled in the class but not on Blackboard, please contact Blackboard help to resolve the issue. We do not add students manually to Blackboard.

After students are in Blackboard, we manually add them to the homework submission system (Vocareum). This can take a few days. Students who register late will be allowed to submit all the assignments they missed because of their late registration.

Completing assignments while on the waiting list

Students who are not registered for the course, including students on the waiting list, may attend the lectures if there is space in the lecture hall. However, only registered students may submit assignments.

Students who are absent from class for any reason must make up the materials themselves, and must submit their assignments on time. Students who register late will be allowed to submit all the assignments they missed because of their late registration.

Introduction to key components of human language technologies, including: information extraction, sentiment analysis, question answering, machine translation.

Students in the course will learn to perform the following:

1. Read technical literature in Natural Language Processing (including original research articles) and answer questions about such readings.

2. Implement language processing algorithms and test them on natural language data.

3. Design, implement, test, and present an original Natural Language Processing application.

The course requires programming in Python. If you are new to Python or haven’t used it in a while, it would be good to brush up on your skills before the course.

• Students who are absent from class for any reason must make up the materials themselves, and must submit their assignments on time. All the assignments will be administered on-line. The course does not have in-class exams.

• The final exam will be administered on-line according to the Final Exam Schedule. University regulations do not allow a student to omit a final examination, or take it in advance of its scheduled time.

Grade Breakdown

• 5% Written application idea (individual, one page)

• 40% Coding assignments

• 10% Quizzes

• 10% Presentations of recent research papers (in groups)

• 30% Research project (in groups)

• 5% Final exam: an on-line, take-home comprehensive exam taken during the final exam period

Grading Policies

• Grading Scale: A: 92%, A–: 90%, B+: 87%, B: 82%, B–: 80%, C+: 77%, C: 72%, C–: 70%, D+: 67%, D: 62%, D–: 60%

• Grades on an assignment or exam will only be changed if there is an error in grading. Grading errors are simple mistakes made on the part of the graders, and not differences in interpretation of a question or answer. A student who wishes to challenge a grade must identify the grading error before asking for a grade change.

• Students are welcome to discuss any aspect of the assignments and exams, but there will be no negotiation on grades, and no changes other than the correction of grading errors.

• There will be a penalty for turning in homework late; the penalty will vary by assignment. In no instance will credit be given for a homework assignment submitted after the solution has been discussed in class.

• Reading quizzes are intended to make sure that students are prepared for class, so no late quizzes will be accepted.

  • One exception: students who registered late will be allowed to make up the quizzes they missed due to late registration. All make-up quizzes will be taken after the add/drop date, in the week of January 31–February 4.

• Homework assignments will not be accepted by email. If there are technical or other issues with the submission system you should write to us and we will work to fix these issues, but do not send homework by email just because you weren’t able to submit it through the system.

• Assignments are usually due at the end of the day. We may not be available to solve issues close to the deadline, so you should plan on submitting your homework early, even if only as a draft. Multiple submissions are generally allowed, and the last submission will be graded. Quizzes may only be submitted once.

• Students are responsible for on-time submission. When submitting your assignments, please allow time to fix issues such as your computer freezing or crashing, network latencies or outages, and so on. If your assignment is submitted late because of a technical issue, it will be penalized as late.

• Students who are not able to make an assignment or exam deadline due to an emergency (for example, a medical emergency) or a conflict (for example, a job interview) must inform the instruction staff as soon as the issue arises, in order to make alternate arrangements. If you wait until after the deadline, or the grades are returned, or the end of the course, you will not be allowed to make up the work.

• Note that in general, conflicts with exams or deadlines for another class do not qualify for making alternate arrangements. Working and studying for multiple classes is an expectation from university students.

• Please use the class discussion boards on Piazza for questions and issues regarding homework assignments and the course in general. This way, the entire class can participate and see the questions and answers.

• Communications of a personal nature should also be sent on Piazza, as a private message to the entire instructional staff.

• Any special requests must be submitted in writing.

The course does not have a textbook. Required readings will be specified in the schedule as the course progresses, and will include a combination of select textbook chapters as well as original research articles. The links below give access to the full text of several textbooks; these are useful for general background on Natural Language Processing, and to supplement some of the materials taught in class. Any chapters that are required will be detailed in the schedule; otherwise, these texts are not required.

• Dan Jurafsky and James H. Martin, Speech and Language Processing (3rd edition draft, 2021)

• Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval (2008) (select chapters)

• Yoav Goldberg, Neural Network Methods for Natural Language Processing (2017) (This link provides free access to the PDF of the full book when accessed from the USC network)

• Jacob Eisenstein, Natural Language Processing (free version, 2018) or the published version (2019)

• Emily M. Bender, Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax (2013) (This link provides free access to the PDF of the full book when accessed from the USC network)

Academic Conduct:

Plagiarism – presenting someone else’s ideas as your own, either verbatim or recast in your own words – is a serious academic offense with serious consequences. Please familiarize yourself with the discussion of plagiarism in SCampus in Part B, Section 11, “Behavior Violating University Standards” policy.usc.edu/scampus-part-b. Other forms of academic dishonesty are equally unacceptable. See additional information in SCampus and university policies on Research and Scholarship Misconduct.

Students and Disability Accommodations:

USC welcomes students with disabilities into all of the University’s educational programs. The Office of Student Accessibility Services (OSAS) is responsible for the determination of appropriate accommodations for students who encounter disability-related barriers. Once a student has completed the OSAS process (registration, initial appointment, and submitted documentation) and accommodations are determined to be reasonable and appropriate, a Letter of Accommodation (LOA) will be available to generate for each course. The LOA must be given to each course instructor by the student and followed up with a discussion. This should be done as early in the semester as possible as accommodations are not retroactive. More information can be found at osas.usc.edu. You may contact OSAS at (213) 740-0776 or via email at osasfrontdesk@usc.edu.

Support Systems:

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