Primary Textbook  (available online)	Additional Resource

 

Course Information

WHERE: Engineering 2: Room 192 (enter from plaza)

WHEN: Mondays Wednesdays 07:10PM-08:45PM

 

Instructor Information

Dilek Hakkani-Tür

email: dhakkani@ucsc.edu 
Office Hours:  TBD

 

TA Information

Wen Cui. email: wcui7@ucsc.edu

 

Lena Reed. email: lireed@ucsc.edu 

 

TA/Tutors & Lab Hours:

Wednesdays 4:00 - 5:30pm in Ming Ong Windows Lab (Merrill Room 103)
Fridays 3:00 - 4:30pm in BE 105

 

 

Online Class Discussion

This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. If you have any problems or feedback for the developers, email team@piazza.com.

Find our class page at: https://piazza.com/ucsc/fall2019/cse14301/home

 

Course Description

Fall 2019. This class introduces advanced undergraduates to the theory and practice of Natural Language Processing. This offering will focus on NLP programming for processing and generation of narratively structured text, such as classic stories such as Aesop's Fables as well as personal narratives that can be mined on the web. CSE 143 provides a combination of homeworks and exams targeted at learning the basics of NLP using the NLTK toolkit and other publicly available software.   You must have previous experience with Python, because we can't teach you both Python and NLP in the class.

Text book:

• Natural Language Processing with Python. Available electronically and from the bookstore. Henceforth referred to as NLLP
• We will be using NLTK 3.0 and the updated version of the online book that corresponds to it. The version of the book in the bookstore is slightly out of date wrt what is on the web.
• We will be using Python 3.0 or later and Jupyter Notebook.

Auxiliary texts:

• Speech and Natural Language Processing. Jurafsky and Martin. Coursera online lectures and parts of book available online.

 

Grading

• Attendance: 5%
• Homeworks and discussion of what we learned from the homeworks in class: 45%
• Homework INCLUDES project, and final presentation of project during Finals slot
• Midterm: 25%
• Final: 25%
• Homework Delivery: Turn it in on Canvas. Please include any code, files, and written documents in a zip file. Written documents should be plain text or PDF only. Multiple uploads (to overwrite) are enabled. Late HW accepted until noon the next day with a 10% penalty. Homeworks not accepted any later than that because the solutions will be posted at noon and discussed in class.