- Course: Natural Language Understanding
- Instructor: Jacob Eisenstein
- Semester: Fall 2014
- Time: Tuesdays and thursdays, 3:05-4:25pm
- TA: Umashanthi Pavalanthan (umashanthi at gatech dot edu)
- Schedule
- Grading
- Policies
This course gives an overview of modern statistical techniques for analyzing natural language. The rough organization is to move from shallow bag-of-words models to richer structural representations of how words interact to create meaning. At each level, we will discuss the salient linguistic phemonena and most successful computational models. Along the way we will cover machine learning techniques which are especially relevant to natural language processing.
- Acquire the fundamental linguistic concepts that are relevant to language technology. This goal will be assessed in the short homework assignments, midterm, and class participation.
- Analyze and understand state-of-the-art algorithms and statistical techniques for reasoning about linguistic data. This goal will be assessed in the midterm, the assigned projects, and class participation.
- Implement state-of-the-art algorithms and statistical techniques for reasoning about linguistic data. This goal will be assessed in the assigned and independent projects.
- Adapt and apply state-of-the-art language technology to new problems and settings. This goal will be assessed in the independent project.
- (7650 only) Read and understand current research on natural language processing. This goal will be assessed in assigned projects and classroom participation.
The assignments, readings, and schedule are subject to change, but I will try to give as much advance notice as possible.
Readings will be drawn from my notes, from published papers and tutorials, and from the following two texts:
- Linguistic Fundamentals for NLP. You should be able to access this PDF for free from a Georgia Tech computer.
- Foundations of Statistical NLP. A PDF version is accessible through the GT library.
These are completely optional, but might deepen your understanding of the material.
- Speech and Language Processing is the textbook most often used in NLP courses. It's a great reference for both the linguistics and algorithms we'll encounter in this course.
- Natural Language Processing with Python shows how to do hands-on work with Python's Natural Language Toolkit (NLTK), and also brings a strong linguistic perspective.
- Schaum's Outline of Probability and Statistics can help you review the probability and statistics that we use in this course.