Fall 2022 / CSCE 771: Computer Processing of Natural Language
Quick Info - When and Where
Tuesday / Thursday 1:15 pm – 2:30 pm
In person at Swearingen 2A24. Recordings to be available on Blackboard.
Instructor Information
Instructor: Biplav Srivastava
E-mail: biplav.s AT sc.edu
Office Hours: 1130-1230 MTh, or appointment
Previous Instances
Attendance Policy
Students are expected to attend in class. When sick, with prior instructor permission, you can attend via Blackboard.
Suggested Reading
Details
Slides and code are available on GitHub [here]. Full details about the course are available in the syllabus [here]. Class schedule is at the bottom of the page.
Bulletin Description
This will be an advanced course on computer-based processing of communication between people (languages) with a focus on text (NLP), and appreciation of multi-modal communication involving speech and images. The course will have lessons on parsing of input, representation of syntax and semantics, and analysis to derive insights. It will explain learning and reasoning based methods for extracting entities, disambiguating and linking them, and applying them for real-world problems. We will also discuss issues in building advanced systems for conversation (“chatbots”) and machine translation, and ethical concerns while testing and fielding them with people.
Prerequisites
Experience with a first course on Artificial Intelligence is desirable, example: CSCE 580. Students without this course may still enroll knowing that necessary AI background can be found in the standard AI book, “AI – A Modern Approach”; http://aima.cs.berkeley.edu/. Necessary material is in chapters 3,4,5 and 6.
Learning outcomes
L1: Appreciate diversity and similarity in natural languages – text, speech and visual; focus of course will, however, be text (NLP) and English
L2: Understand issues related to data and tools. Experiment design, Metrics for evaluation and to detect bias, Methods to build trust in processing – transparent assessment, Providing explanations for output
L3: Data processing: (a) Structured data representation from unstructured text; (b) Extract entities and relationships; (c) Extract contexts; (d) representation learning – word embedding
L4: AI methods in NLP: (a) Learning methods – including language models, (b) Reasoning, (c) Representation – knowledge graphs/ ontology
L5: NLP applications – (a) Document intelligence: sentiment, translation; (b) collaborative assistants
Grading Schema
Project: 50% for project report and code, plus 10% for elevator presentation to class
Quiz: 20% (3 out of 4)
Exam: Paper reading and presentation to class: 20%
