30 Essential NLP Skills

Natural language processing (NLP) is a field of computer science that focuses on giving computer and artificial intelligence (AI) models the ability to understand natural human languages. This is an important technology used for applications like sentiment analysis, text generation, machine translation, and spam detection. If you are considering a career as a natural language processing engineer, you will need to gain the core competencies, technical skills, data analysis skills, and workplace skills needed to be successful in this field. 

Explore the core competencies of NLP, such as understanding NLP tasks and AI models, the technical skills you’ll need, such as programming languages, machine learning techniques, and computational linguistics, and workplace skills, such as teamwork and collaboration, that are important for a career as an NLP engineer. 

What is natural language processing (NLP)?

Natural language processing is the technology that allows a computer or AI algorithm to understand natural human language, language that evolved in humans naturally, like English, Chinese, or Spanish. NLP works by using machine learning, deep learning, and neural networks to understand the patterns, structure, grammatical rules, and probabilities within languages. Using this understanding, the AI model can perform NLP tasks like generating realistic-looking text, translating between languages, or summarizing long passages in a few short sentences. 

What is an NLP engineer?

An NLP engineer is a professional who designs, creates, and trains NLP systems and algorithms. You may work to add NLP functionality to software programs, create new methods of understanding natural language with AI, or work to improve the ability and analytical skills of existing NLP models. In this role, you can work to deploy NLP solutions in industries like entertainment, retail, finance, education, national defense, marketing, health care, and more. 

Core competencies in natural language processing

A few of the key things you’ll need to gain experience in to work as a natural language processing engineer include understanding some of the main tasks you’ll use natural language processing for, understanding the machine learning and AI models you’ll use for NLP, and understanding the computational linguistics and text processing you’ll need to make sense of large amounts of data. Explore some of the specific skills you’ll need in each category. 

Understanding NLP tasks

You can use NLP to accomplish a wide range of tasks, each with its own applications in different industries. For example, you can use NLP to create an AI model that generates text in a variety of styles, which could then be used in different applications, from entertainment to education to marketing to creative writing.

Some of the ways you can use NLP that you should be familiar with include: 

• Sentiment analysis: Sentiment analysis analyzes text to classify the tone as negative, positive, or neutral. Marketing professionals can use this data to inform their brand strategy. 

• Toxicity classification: You can use natural language processing to detect or filter comments based on toxicity, such as hateful or violent speech. 

• Machine translation: Using machine learning and artificial intelligence, you can use natural language processing to translate from one language to another. 

• Spam detection: Natural language processing can help detect spam in emails and online to flag or filter these results. 

• Text generation: Automatically generating text that looks like a human could have written it in various styles. 

• Summarization: You can use natural language processing to summarize the main points of large bodies of text. 

Understanding AI models for NLP

You will use artificial intelligence models to analyze natural language as an NLP engineer. You must understand statistical modeling, topic modeling, deep neural networks, and language models. 

• Statistical modeling: Statistical modeling forms the basis of language models by analyzing the text in various ways, each looking at a different component of the probability of the arrangement of the text. 

• Topic modeling: Topic modeling is a method for analyzing the abstract concepts or topics within a text. 

• Deep neural networks: Deep learning is another component of creating language models. It works by calculating the probability at every word within a document that a given word will appear in that position. 

Familiarity with computational linguistics and text-processing techniques

To be successful in NLP, you will also need to develop skills in analyzing natural languages, such as part-of-speech tagging and named entity recognition. You will need to understand pre-processing tasks like stemming and lemmatization, sentence segmentation, stop word removal, and tokenization. 

• Part-of-speech tagging: A method for tagging parts of speech, for example, nouns, verbs, and adjectives. 

• Named entity recognition: A method to identify entities with names, such as people or places that are called something other than what they are, for example, “Green Gables,” instead of “a farmhouse.” 

• Stemming and lemmatization: Methods for finding the root of words. For example, libraries, library, library’s, and librarians, all share a root word that informs the meaning.

• Sentence segmentation: A method for breaking a sentence down into smaller units. 

• Stop word removal: A method to simplify data by removing “stop words”: such as “the”, “a”, and “as”. 

• Tokenization: A method of simplifying text by translating tokens, or small parts of words or sentences, into a numerical representation. 

Technical skills

As an NLP engineer, you will need to have technical skills to use the tools and resources for natural language processing. For example, you’ll need to know how to work with programming languages, structure a machine learning workflow with MLOps, machine learning techniques, how specific language models work, and many libraries and tools relevant to NLP. 

The technical skills you’ll need as a natural language processing engineer include: 

• Programming languages like C, C++, Python, Java, and R

• MLOps and tools like MLFlow and AirFlow

• Machine learning techniques like feature extraction

• Understanding specific language models like BERT, GPT, and LaMDA

• Deep learning libraries like TensorFlow and PyTorch

• NLP libraries like spaCy and Natural Language Toolkit (NLTK)

Data analysis

Data analysis is another important NLP skill because many of the tasks you will use natural language processing for are data analysis tasks. For example, sentiment analysis is, in simple terms, an analysis of the data you can gain from written statements. You can use your computational linguistics and text processing skills to analyze text granularly and draw meaning from the data. 

Communication skills

As a natural language processing engineer, you will also need communication skills. You will often work in collaborative groups with other NLP engineers and computer or data scientists, so you will need to develop the skills that make it easier to work in teams, like conflict management, leadership, and collaboration. You will also report your NLP work to stakeholders and non-technical leadership in your organization. You will also need to deploy creative solutions, which requires you to have creativity and problem-solving skills. You may also find it necessary to develop experience in the field for which you’re creating NLP solutions because you may find that some industries develop a natural language of their own, such as key terms for the work or a common lingo. 

The communication skills you may need as a natural language processing engineer include: 

• Understanding the language of the industry you design for

• Creativity

• Motivation

• Conflict management

• Collaboration

• Organization

• Time management 

• Leadership

Learn NLP skills on Coursera

NLP engineers need to know core competencies in NLP, technical skills specific to the technology you’ll need to power NLP programs, and workplace skills that make it easier to work as a team. You can start learning the skills you’ll need to be successful on Coursera. For example, you could earn a Deep Learning Specialization offered by Deep Learning.AI to learn about machine translation, transformers, sentiment analysis, and attention models.