Word-sense disambiguation (WSD) is a well-known problem in NLP. First of all, let's understand what WSD is. WSD is used in identifying what the sense of a word means in a sentence when the word has multiple meanings. When a single word has multiple meaning, then for the machine it is difficult to identify the correct meaning and to solve this challenging issue we can use the rule-based system or machine learning techniques.
In this chapter, our focus area is the RB system. So, we will see the flow of how WSD is solved. In order to solve this complex problem using the RB system, you can take the following steps:
- When you are trying to solve WSD for any language you need to have a lot of data where you can find the various instances of words whose meaning can be different from sentence to sentence
- Once you have this kind of dataset...