Social media provides a massive amount of valuable information and shows us how language is actually used by lots of people. This course will give an overview of prominent research findings on language use in social media. The course will also cover several machine learning algorithms and the core natural language processing techniques for obtaining and processing Twitter data.
- Wei Xu is an assistant professor in the Department of Computer Science and Engineering at the Ohio State University. Her research interests lie at the intersection of machine learning, natural language processing, and social media. She holds a PhD in Computer Science from New York University. Prior to joining OSU, she was a postdoc at the University of Pennsylvania. She is organizing the ACL/COLING Workshop on Noisy User-generated Text, serving as a workshop co-chair for ACL 2017, an area chair for EMNLP 2016 and the publicity chair for NAACL 2016.
- Fall 2017, CSE 5539-0010 The Ohio State University
Bolz Hall Room 318 | Tuesday 2:20PM – 4:10PM
dual-listed undergraduate and graduate course
[Office Hour] Dreese 495 | Tuesday 4:15PM – 5:15PM
- In order to succeed in this course, you should know basic probability and statistics, such as the chain rule of probability and Bayes’ rule; some basic calculus and linear algebra will also help, such as knowing what is gradient. On the programming side, all projects will be in Python. You should understand basic computer science concepts (like recursion), basic data structures (trees, graphs), and basic algorithms (search, sorting, etc).
- Various academic papers
- This is a research-oriented project-based course (total 100 points). Instead of exams, each student will do two hands-on programming assignments and an optional research project. The class will assign one paper for reading each week. Each student should read the assigned paper and submit a short critique (between 100-200 words) online in Carmen before 10:00am on the day of class. These reviews should not be simple summaries, but discuss positive aspects of the paper and limitations (some examples), or suggestions for how the work could be improved or extended. Students are allowed to skip two reviews throughout the semester. Please email your reviews or homeworks to the instructor if there are any technical issues with online submission.
- programming homework #1 (15 points/individual)
- programming homework #2 (30 points/individual)
- take-home quizzes (10 points/individual, about 5 quizzes)
- paper summaries (20 points/individual, about 10 papers)
- in-class presentation (20 points/group of two)
- participation in class discussions (5 points/individual)
- a research project or programming homework #3 (optional, 20 bonus points)
- Fall 2016, The Ohio State University
Summer 2016, The North American Summer School on Logic, Language, and Information (NASSLLI)
Teaching evaluation was 5.72 out of 6 at NASSLLI; average across all instructors was 5.23.
Summer 2015, University of Pennsylvania