Careers In AI

Mar

Friday

10

Top 3 Skills Needed For NLP (Natural Language Processing) Engineers

If you’re reading this article, you probably want to become an NLP engineer. Becoming an NLP Engineer is like being a language detective with technical superpowers.  

For those new to the field, NLP might seem like a complex and daunting area to dive into. It can be difficult to know where to start and what to focus on when building a career in NLP. 

Should you immediately dive into the deep end and start studying Neural Networks, or take a more foundational approach and start with the basics like text normalization?

To get started in NLP, it's essential to build a strong foundation by studying the fundamentals. These include linguistic theory, natural language understanding, and statistical analysis. 

From there, you can dive into more advanced concepts like machine learning and deep learning. Let’s take a quick look at some of the top skills you need to acquire as an NLP engineer:

1. Data extraction

Over the past decade, the internet has been flooded with an overwhelming amount of textual data. This makes it necessary for NLP practitioners to possess a variety of skills for dealing with text data from different sources and in various formats.

Ingesting text data from sources such as CSV and JSON files is a crucial skill for anyone pursuing a career in NLP. These file formats are commonly used for text corpora and must be imported into your workspace before NLP applications can be implemented.

By acquiring data ingestion skills, you can gain access to a wealth of data, ultimately enhancing your NLP applications and producing better results. And, who knows, you might just uncover some fun and interesting nuggets of information along the way. 

2. Text Classification

Text classification is the process of assigning text to specific categories using predictive models. NLP engineers use a variety of models including tree-based models, Naive Bayes classifiers, or neural networks. 

Text classification is commonly used for tasks such as sentiment analysis, spam detection, and text categorization.

Learning text classification techniques is essential for NLP practitioners as it involves applying several fundamental concepts such as text normalization, n-grams, and stemming. 

Building a simple sentiment classifier for tweets is a good way to start and gain an understanding of how to build classification models. With this knowledge, you can apply it to more complex tasks and datasets.

3. Programming

If you’re passionate about NLP engineering, you have to develop certain programming skills 

As an NLP engineer, you are likely to develop software that can understand and respond to human language. You have to use various techniques, such as text representation, semantic extraction, and modeling to create effective algorithms that can accurately process and analyze text data.

To become an NLP engineer, you'll need strong programming skills in languages like Python, Java, and C++. 

Experience with machine learning frameworks and libraries, such as TensorFlow or PyTorch will be a plus. Familiarity with big data frameworks like Hadoop and Spark is also essential in this field.

4. Other skills an NLP engineer must possess

  • Knowledge of text representation techniques, algorithms, and statistics.
  • Familiarity with Machine Translation and Compilers, as well as machine learning frameworks and libraries, is necessary.
  • Knowledge of Spark and Hadoop, two big data frameworks. 
  • Proficiency in programming languages like Python, Java, and R, is required.
  • Strong problem-solving skills are a must.
  • NLP Engineers should have knowledge of syntactic and semantic parsing.
  • Understanding of Continuous Integration and Continuous Delivery (CI/CD) pipelines is necessary.
  • Excellent communication skills are needed to interact effectively within the organization.

Closing thoughts

To stay up-to-date in this rapidly evolving field, it's important to remain curious and adaptable. Keep an eye out for the latest tools and techniques, and don't be afraid to experiment and try new things. 

And, of course, don't forget to inject some humor and fun into your work - after all, NLP is all about understanding and communicating with people, so why not make it enjoyable?