Linguistics Department

Jessy Li


Assistant ProfessorPh.D., University of Pennsylvania

Jessy Li

Contact

Biography


Junyi Jessy Li is an assistant professor in the Linguistics Department at the University of Texas at Austin. She earned her Ph.D. (2017) in Computational Linguistics from the Department of Computer and Information Science at the University of Pennsylvania. Her research interests are in computational linguistics and natural language processing with a focus on discourse and pragmatics. Her work has explored discourse structure, text specificity, and language in social media. She received an ACM SIGSOFT Distinguished Paper Award in 2019, an Area Chair Favorite honor at COLING 2018, and a Best Paper Award nomination at SIGDIAL 2016.

Courses


LIN 373 • Machine Learning Text Analysis

39770 • Spring 2020
Meets TTH 2:00PM-3:30PM RLM 5.114
IIQR

Technology that automatically analyzes text has made amazing strides, and lets us do things like automatically translate from Chinese to English, summarize what people on Twitter think about some current political topic, or find clues on who the author is of some classic piece of literature. Machine learning plays a central role in this technology: software that can learn from experience. This course provides an overview of basic statistical methods for machine learning, with an emphasis on applications that have to do with text. Topics include supervised learning (e.g., logistic regression, support vector machines, neural networks) and unsupervised learning (e.g., clustering, dimensionality reduction). This is a hands-on course with Python programming.

Prerequisites: Basic knowledge in (Python) programming is assumed. Prior knowledge of elementary probability is preferred.

LIN 389C • Rsch In Computatnl Linguistics

39815 • Spring 2020
Meets T 10:00AM-1:00PM RLP 4.422

This course will be a combination of discussion and presentations and will cover topics such as recent trends in computational linguistics and machine learning and software and tools for data analysis. We will focus on research design, data sources, presentation and publication of research data and analysis, and dissertation writing. The course will give students the opportunity to pursue their own research in a guided, collegial environment. Graduate students from other departments are welcome!

LIN 313 • Language And Computers

39295 • Fall 2019
Meets TTH 11:00AM-12:30PM JES A216A
QR

This undergraduate class looks at everyday tasks that involve natural language processing: document classification, spelling and grammar correction, dialogue systems, machine translation, cryptography and forensic linguistics. Students will get insight into the how these systems work (and why it is still so difficult to do natural language processing well). We also consider social and ethical considerations such as privacy, job creation and loss due to language technologies, and the nature of consciousness and machine intelligence.

 

LIN 393 • Comp Ling: Minimal Supervsn

39435 • Fall 2019
Meets TTH 2:00PM-3:30PM RLP 4.422

Recent progress in machine learning, especially in deep learning, has led to encouraging advances in Natural Language Processing: state-of-the-art results on benchmark datasets are getting renewed at a rapid pace. However, most of these methods rely on a large amount of labeled training data. Yet, high quality labeled textual data is expensive and difficult to build; the amount of labeled data is destined to be small compared to the vastness and dynamics of language (i.e., the number of different languages, possible genres/domains, and language evolution). In this seminar, we discuss several ways to address this bottleneck, including semi-supervised learning, noisy supervision, transfer learning, and domain adaptation. We focus on the unique challenges of natural language understanding and generation tasks.

 

Prerequisites: Graduate standing.  Prior coursework in Computational Linguistics/Natural Language Processing/Machine Learning/a related field in AI, or instructor consent.

LIN 373 • Machine Learning Text Analysis

40135 • Spring 2019
Meets TTH 11:00AM-12:30PM SZB 380
QR

Technology that automatically analyzes text has made amazing strides, and lets us do things like automatically translate from Chinese to English, summarize what people on Twitter think about some current political topic, or find clues on who the author is of some classic piece of literature. Machine learning plays a central role in this technology: software that can learn from experience. This course provides an overview of basic statistical methods for machine learning, with an emphasis on applications that have to do with text. Topics include supervised learning (e.g. logistic regression, support vector machines, neural networks) and unsupervised learning (e.g., clustering, dimensionality reduction). This is a hands-on course with Python programming.

Prerequisites: Prior knowledge of elementary probability is preferred. Programming experience is not required; the course includes an introduction to programming in Python.

LIN 389C • Rsch In Computatnl Linguistics

40170 • Spring 2019
Meets F 12:00PM-3:00PM RLP 4.422

This course will be a combination of discussion and presentations and will cover topics such as recent trends in computational linguistics and machine learning and software and tools for data analysis. We will focus on research design, data sources, presentation and publication of research data and analysis, and dissertation writing. The course will give students the opportunity to pursue their own research in a guided, collegial environment. Graduate students from other departments are welcome!

LIN 313 • Language And Computers

40245 • Fall 2018
Meets TTH 11:00AM-12:30PM BIO 301
QR

This undergraduate class looks at everyday tasks that involve natural language processing: document classification, spelling and grammar correction, dialogue systems, machine translation, cryptography and forensic linguistics. Students will get insight into the how these systems work (and why it is still so difficult to do natural language processing well). We also consider social and ethical considerations such as privacy, job creation and loss due to language technologies, and the nature of consciousness and machine intelligence.

 

LIN 393S • Computational Discourse

40380 • Fall 2018
Meets TTH 2:00PM-3:30PM RLP 4.422

Written text usually consists of multiple sentences; to fully understand a text as a whole requires information that cannot be obtained when considering each sentence individually.  In this seminar course, we look at discourse processing: how references to entities, and relationships between clauses and sentences (e.g., cause, result, elaboration), contribute to the local coherence of the text.  We will study the following aspects: frameworks, corpora, and computational models (e.g., coreference resolution and discourse parsing).  We will also discuss discourse processing in the context of a number of Natural Language Processing tasks, such as summarization, question answering, and sentiment analysis.

 

Prerequisites: Graduate standing.  LIN 353C or CS 388 or CS 395T or equivalent prior exposure to Computational Linguistics/Natural Language Processing.

LIN 313 • Language And Computers

40463 • Spring 2018
Meets TTH 2:00PM-3:30PM CLA 1.108
QR

This undergraduate class looks at everyday tasks that involve natural language processing: document classification, spelling and grammar correction, dialogue systems, machine translation, cryptography and forensic linguistics. Students will get insight into the how these systems work (and why it is still so difficult to do natural language processing well). We also consider social and ethical considerations such as privacy, job creation and loss due to language technologies, and the nature of consciousness and machine intelligence.

 

LIN 389C • Rsch In Computatnl Linguistics

40585 • Spring 2018
Meets W 9:30AM-12:30PM CLA 4.422

This course will be a combination of discussion and presentations and will cover topics such as recent trends in computational linguistics and machine learning and software and tools for data analysis. We will focus on research design, data sources, presentation and publication of research data and analysis, and dissertation writing. The course will give students the opportunity to pursue their own research in a guided, collegial environment. Graduate students from other departments are welcome!

Curriculum Vitae


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External Links



  • Department of Linguistics

    University of Texas at Austin
    305 E. 23rd Street STOP B5100
    Robert L. Patton Hall (RLP) 4.304
    Austin, TX 78712
    512-471-1701