Part 9: Step by Step Guide to Master NLP – Semantic Analysis
This article was published as a part of the Data Science Blogathon
Introduction
This article is part of an ongoing blog series on Natural Language Processing (NLP). In the previous article, we discussed some important tasks of NLP. I hope after reading that article you can understand the power of NLP in Artificial Intelligence. So, in this part of this series, we will start our discussion on Semantic analysis, which is a level of the NLP tasks, and see all the important terminologies or concepts in this analysis.
This is part-9 of the blog series on the Step by Step Guide to Natural Language Processing.
Table of Contents
1. What is Semantic Analysis?
- Difference between Semantic and Lexical Analysis
- Two parts of Semantic Analysis
2. Semantic analysis with Machine Learning
- Word Sense Disambiguation
- Relationship Extraction
3. Elements of Semantic Analysis
- Hyponymy
- Homonymy
- Polysemy
- Synonymy
- Antonymy
- Meronomy
4. Meaning Representation
- Building Blocks of Semantic System
- Approaches to Meaning Representations
- Need of Meaning Representations