Advanced Semantic Processing

Overview

Class Central Tips

Start your journey into advanced semantic processing with an introduction to fundamental concepts such as entities, arity, and reification. Learn about schemas and semantic associations, understanding how these elements form the backbone of semantic processing. The course covers important concepts like terms, the principle of composition, and tools like WordNet and word sense disambiguation, culminating in a case study using the Lesk algorithm. Move forward with an in-depth exploration of distributional semantics, delving into occurrence matrices and co-occurrence matrices to understand semantic relationships. Develop proficiency in creating and interpreting word vectors and grasp the importance of distance metrics. This section equips you with the skills needed to handle large sets of textual data, extracting meaningful semantic information. Finally, tackle advanced topics such as Latent Semantic Analysis (LSA) and Word2vec, applying these techniques to real-world scenarios through multiple case studies. By the end, you will have a robust understanding of advanced semantic processing techniques and their applications in NLP. This course is designed for NLP enthusiasts, data scientists, and professionals looking to deepen their knowledge of semantic processing. A basic understanding of NLP and machine learning concepts is recommended.

Syllabus

Introduction to Semantic Processing

In this module, we will delve into the foundational aspects of semantic processing. Starting with basic concepts and entities, we will explore the intricacies of arity, reification, and schemas. The module will also cover semantic associations, terms, concepts, and culminate with practical applications like WordNet and word sense disambiguation.

Advanced Semantic Processing: Part 1

In this module, we will introduce advanced topics in semantic processing, focusing on distributional semantics. We'll explore the concepts and applications of occurrence and co-occurrence matrices, delve into word vectors and their significance, and understand the metrics used to measure semantic similarity and differences.

Advanced Semantic Processing: Part 2

In this module, we will continue our exploration of advanced semantic processing techniques. We'll cover Latent Semantic Analysis (LSA) and Word2vec in depth, supported by multiple case studies to demonstrate their practical applications. Additionally, we will investigate the use of these techniques in various classification scenarios to solidify our understanding.