The following are courses offered at UF related to computational linguistics (also known as natural language processing or NLP).
LIN 4930/6932: PYTHON PROGRAMMING FOR LINGUISTICS
Course Description
This class presents techniques computer programming in the high level programming language Python. The students learn to address a range of problems with a specific focus on language processing and linguistics, such as the discovery of morphemes in a new languages. The class is suitable for students with little to no prior experience in computing or programming.
When is this course offered?
Fall
Prerequisite
LIN 3010: Introduction to Linguistics, or permission
Contact
Dr. Sarah Moeller
LIN 4770C/5770C: INTRODUCTION TO COMPUTATIONAL LINGUISTICS
Course Description
This course surveys selected topics and tasks that are fundamental to computational linguistics. Students will learn to gain (1) understanding of different linguistic problems that could be solved with mathematical means; (2) understanding of different computational methods to derive automatic analysis of language structures at different linguistic levels; (3) technical programming skills to model linguistic phenomena in a computational framework.
When is this course offered?
Spring
Prerequisite
An interest in Linguistics is required. Prior coursework in programming is not required
Contact
Dr. Zoey Liu or Dr. Sarah Moeller
CAP 4641: NATURAL LANGUAGE PROCESSING
Course Description
Introduction to the essential concepts, principles, and techniques of Natural Language Processing (NLP). Practical application and theoretical concepts are examined. Topics include information extraction, language construction, grammars, disambiguation, as well as system modeling, classification, and evaluation.
When is this course offered?
Last offered in Fall 2022
Prerequisite
COP 3530: Data Structures & Algorithms
Contact
Dr. Bonnie Dorr
CIS 4930: NATURAL LANGUAGE PROCESSING WITH PYTHON
Course Description
Introduction to the essential concepts, principles, and techniques of Natural Language Processing (NLP). Practical application and theoretical concepts are examined. Topics include information extraction, language construction, grammars, disambiguation, as well as system modeling, classification, and evaluation.
When is this course offered?
Last offered in Spring 2021
Prerequisite
COP 3530: Data Structures & Algorithms
Contact
Dr. Pete Dobbins
CIS 4930/6930: DIALOGUE SYSTEMS AND NATURAL LANGUAGE INTERFACES
Course Description
Dialogue systems play an increasingly important role in our daily lives. In this course, we will learn step-by-step how spoken dialogue systems are created, including automatic speech recognition and generation, natural language understanding techniques, dialogue management and domain modeling, and voice user interface design. Students will work in groups to complete a final project that applies and extends what they have learned in class. The course features an undergraduate and a graduate track. At the completion of the class, students will have a broad set of knowledge and skills that they can apply in any situation that involves processing natural language. The course format will consist of lectures, in-class discussions, and student presentations. Examinations will consist of material covered in lectures and projects.
When is this course offered?
Last offered in Spring 2021
Prerequisite
Undergraduate Data Structures class or equivalent, or permission of the instructor
Contact
Dr. Kristy Elizabeth Boyer
EEL 6935: MACHINE LEARNING FOR NATURAL LANGUAGE PROCESSING
Course Description
The goal of natural language processing is to allow machines to understand and process human language. This course extends the knowledge presented in EEL-5840 Elements of Machine Intelligence to understand how machine learning methods can be applied to natural language processing. During the first part of the course, fundamental concepts and methods used in natural language processing are introduced. During the second portion of the course, more recent machine learning-based approaches, particularly neural networks/deep-learning are presented.
When is this course offered?
Last offered in Spring 2019
Prerequisite
EEL 5840: Elements of Machine Intelligence
Contact
Dr. Damon L. Woodard
GMS 6856: INTRODUCTION TO BIOMEDICAL NATURAL LANGUAGE PROCESSING
Course Description
This course will examine current natural language processing (NLP) methods and their applications in the biomedical domain. This introduction will cover the basic knowledge of general NLP, basic NLP tasks at different linguistic levels, NLP applications in biomedical literature and clinical text, ontologies and resources in the biomedical domain, popular NLP methods and machine learning models, commonly used NLP tools, as well as relevant computational linguistic knowledge. It will provide hands on experience with existing biomedical NLP systems. Students will gain knowledge and skills in various NLP tasks such as Named Entity Recognition, Information Extraction, and Information Retrieval.
Prerequisite
Experience with computer programming, such as Python for data processing
Contact
Dr. Yonghui Wu
EDF 6938: INTRODUCTION TO NLP IN EDUCATION
Course Description
This course is designed to introduce the basic concepts and techniques of natural language processing in education research. We will focus on text mining techniques and natural language understanding approaches commonly used in education text analysis. Students will have opportunity to survey the NLP literature in the emerging AI education research context to acquire theoretical backgrounds to understand the methods, and to gain hands-on experience in education text analysis using Python. The primary topics will include, but are not limited to, text vectorization, factor analysis and dimensionality reduction, supervised, unsupervised and deep learning in text analysis. Two primary learning components of this course include the theoretical and mathematical aspects of NLP and the hands-on programming experience in NLP analysis using Python.
When is this course offered?
Fall
Contact
Dr. Jinnie Shin
DSI NATURAL LANGUAGE PROCESSING WORKSHOP
Course Description
This workshop covers introductory techniques and resources for NLP and ends with the implementation of a machine learning topic mining algorithm. No advanced coding experience required.
Notes
This workshop is free, no credit (consider independent study)