What This Document Is
This is a detailed assignment specification for a project within an Introduction to Natural Language Processing course. It focuses on applying n-gram modeling techniques to improve the efficiency of Augmentative and Alternative Communication (AAC) systems, specifically those utilizing a scanning-based input method. The assignment challenges students to develop a system that predicts letters to reduce the time it takes for individuals relying on AAC devices to communicate. It outlines a practical problem with a real-world application, requiring students to make informed decisions about data selection, model parameters, and evaluation methodologies.
Why This Document Matters
This assignment is crucial for students seeking to understand the practical application of NLP concepts. It’s particularly valuable for those interested in human-computer interaction, assistive technologies, or the intersection of linguistics and computer science. Students will gain hands-on experience in building and evaluating a predictive model, a skill highly sought after in various NLP roles. Successfully completing this assignment demonstrates an ability to translate theoretical knowledge into a functional solution for a specific communication challenge. It’s ideal for students preparing for more advanced coursework or research in NLP.
Topics Covered
* N-gram Modeling
* Text Corpus Selection and Preparation
* Smoothing Techniques for N-gram Models
* Evaluation Metrics for Predictive Systems
* Scanning-based AAC Systems
* Communication Rate Optimization
* Cross-Validation Techniques
* Performance Analysis and Reporting
What This Document Provides
* A clear problem statement outlining the goal of letter prediction in AAC systems.
* Detailed instructions for developing and evaluating prediction models.
* Specific requirements for creating modules to measure typing time with and without prediction.
* Guidelines for establishing a common testing set to facilitate comparison between student projects.
* A framework for reporting on corpus characteristics and model performance, including time savings analysis.
* Instructions for conducting experiments to determine optimal prediction window sizes.
* Submission requirements, including code and test results.
