How to learn NLP

So what do you need to know to start doing natural language processing? Do you need a computer science degree?

You don`t need any degree to become an NLP specialist. All you need is to learn and practice certain skills, and create several projects to prove your knowledge. Getting started in natural language processing can be tricky. The amount of information on the internet is overwhelming and can be confusing or misleading. As a person who went through this myself, I decided to write an article and share a short and clear guide to get started.

1. Basics of linguistics

Basically, NLP is about learning languages - for example, it used by tools like https://www.conveythis.com//. The developer is trying to explain to the computer how to understand the tricky written and spoken language of a person. I took up NLP because I was always interested in languages ​​and how they formed and developed over time. However, speaking a language does not mean fully understanding its logic.

To have a solid foundation in getting started with NLP, you must fully understand the underlying logic of the language you are trying to "teach" the computer. This language does not have to be your first language. You can even learn a new one while developing a project to analyze it. I don`t mean to get a degree in linguistics or anything like that. What I`m trying to say is that understanding how languages ​​solve various problems can be helpful in developing and analyzing NLP applications. Moreover, knowing about the cross-language influence, you can create multilingual applications. I recommend starting with Emily M. Bender Linguistic Fundamentals for Natural Language Processing by Emily M. Bender.

2. String manipulation

The "language" in which you are trying to parse and build applications is usually in the form of strings. Even if it`s a speech recognition app, it is still converted to text before being parsed.

Therefore, the first step you need to master before diving into basic NLP techniques is string manipulation using any programming language.

If you have no programming experience, I recommend starting with Python. It is widely used in various fields of data science including NLP. If you already have programming experience in other languages, then mastering string manipulation will not be difficult.

3. Regular expressions

Once you have mastered string manipulation with built-in functions in your programming language of choice, the next step is regular expressions. It is one of the most powerful and efficient word processing techniques. Regular expressions have their own terminology, conditions, and syntax. Some developers see them as a mini-programming language. They will help you generalize the rules and make your test-handling applications more efficient. See Wiki for more info.

4. Data cleaning

The quality of the work result depends on the input data. Therefore, it is important that they are prepared in the best possible way. This skill is applicable not only to NLP projects, but to all areas of data science. However, approaches to data cleansing differ depending on the objectives and target outcomes. When preparing text for processing and analysis, we usually remove all punctuation marks. This helps to improve the variability of words in the text. There are also different types of words, such as stop words, that you can remove for more efficient analysis.