LING 1330/2330 Introduction to Computational Linguistics
Fall 2020, University of PittsburghMeetings: Tue & Thu 4:30 - 5:45pm Classroom: 11 Thaw Hall
DescriptionThis is a course designed to introduce students who have been exposed to linguistics to real-world applications of computational linguistics. The students will first learn the fundamentals of how computers are used to represent and process textual and spoken information. They will then be introduced to the challenges of real-world language engineering problems and learn how they are handled with the latest language technologies. The topics include: spell-checking, machine translation, part-of-speech tagging, parsing, document classification, and corpus building and exploration. Students will be given hands-on training on the basics of text processing using Python and will have a chance to work with NLTK, a popular natural language processing application suite. This course is designed specifically for students in the humanities; computer science majors (who are not linguists) are encouraged to take CS 1671 or CS 1571 instead.
PrerequisitesIntro-level linguistics and Python knowledge are required: LING 1000 "Introduction to Linguistics" and CS 0008 "Introduction to Computer Programming with Python" (grade B or above). Having Python programming as a prerequisite will allow us to explore more computational linguistic topics and in a less rushed manner. Linguistics majors and grad students will very much remain as the target audience of this course: as a matter of fact, not having to learn Python will free up valuable class time to focus more on linguistic motivations.
Students are required to bring their own laptop to class. It should be running one of the following operating systems: Windows 10 (7 & 8 are also fine), Mac OS-X, and Linux (any distribution). Mobile or cloud-based machines such as Android/Apple tablets or Chromebooks are not suited.
Textbooks Language and Computers. Markus Dickinson et al. Wiley-Blackwell. 2012.
 Speech and Language Processing 2nd Edition, 3rd Edition. Jurafsky & Martin.
 Natural Language Processing with Python. (updated edition based on Python 3 and NLTK 3) Steven Bird et al. O'Reilly Media.
 Python tutorial: Python 3 Notes
Course OrganizationEach meeting will have lecture and lab components. Topics presented in the textbooks will be covered in a lecture-and-discussion format. In lab, students will get hands-on training using Python and Natural Language Toolkit (NLTK). "Learning by doing" is the core design principle of this class!
We will be using four (!!) platforms: this course home page (everything that's public: syllabus, policies, course schedule, class materials such as lecture slides, homework and exercise assignments), Canvas (private things: course announcements, assignment submission, grades), Zoom (administered through Canvas, recordings will be made available), and MS Teams (chat-based communication, holding office hours, one-on-one video calls).
Assignments, Requirements, Grading and PoliciesAs a rule, there will always be a form of assignment between classes. There are two types: homework assignments and programming exercises, which are administered via Canvas and due before the beginning of the next class. Assignment schedule is posted on the Class Schedule page. Details on all requirements, grading and other policies can be found on the Policies page.
Flex@Pitt and This CourseJust like all classes at Pitt this semester, this course will be following Pitt's Flex@Pitt model. This means the delivery mode of this class may change based on Pitt's operating posture at the moment. Under "HIGH RISK", all courses are remote; under "ELEVATED RISK", most courses are remote, likely including this one. In "GUARDED RISK" requiring in-person components, I have two plans:
With the exception of the mode of delivery, I expect all other aspects of this course will remain unchanged throughout different operational postures, including dissemination of course materials, course requirements, and grading policies. They are detailed in the Course Policies page.
COVID-19 Statement (Pitt Official)In the midst of this pandemic, it is extremely important that you abide by public health regulations and University of Pittsburgh health standards and guidelines. While in class, at a minimum, this means you must wear a face covering and comply with physical distancing requirements; other requirements may be added by the University during the semester. These rules have been developed to protect the health and safety of all community members. Failure to comply with these requirements will result in you not being permitted to attend class in person and could result in a Student Conduct violation. For the most up-to-date information and guidance, please visit coronavirus.pitt.edu and check your Pitt email for updates before each class.