9 Essential Natural Language Processing Skills

Key takeaways

Natural language processing (NLP) is a highly specialized subfield of artificial intelligence (AI) that focuses on enabling computer programs to understand human language. At a glance, here's what you need to know about skills you'll need to become an NLP professional:

• Some experts consider NLP among the most useful applications of AI in use today because it allows even non-technical users to leverage the technology.

• Key skills include understanding AI models, familiarity with text-processing techniques, knowledge of programming languages, and capability with machine learning algorithms (among many other things).

• Building your skills is essential to landing a job in the field, such as NLP engineer .

Below, you'll learn more about the essential natural language processing skills needed to advance your career. Afterward, if you want to build your NLP skills, consider enrolling in DeepLearning.AI's Natural Language Processing Specialization.

Core competencies in natural language processing

Working in NLP requires a solid knowledge base and a few core competencies: the ability to work with language models, text processing techniques, and sentiment analysis. 

1. Understand AI and language models

Understanding a variety of core NLP models is essential. Examples include: 

• Transformer models: Transformer models utilize a complex set of neural networks to change input sequences into output sequences. They learn how to discover patterns in input data and then recombine pieces of that data into relevant answers to prompts. Because transformers process data in parallel, they can train extraordinarily sophisticated LLMs. Programmers often prefer transformer models over older convolutional neural networks (CNN) and recurrent neural networks (RNN). 

• GPT: The generative pre-trained transformer, a subtype of transformer model, can create articles, reports, product descriptions, and much more via human prompts. One example is the popular ChatGPT. The GPT model’s main value proposition is that it automates the generation process very quickly and at an extraordinary scale. 

• BERT: Google developed its open-source Bidirectional Encoder Representations from Transformers (BERT) as a transformer model that programmers can train more quickly. BERT teaches a language program to read sentences left to right and right to left, enabling it to understand the semantic meaning of individual words in a sentence. This means BERT-trained models more quickly learn to identify that, for example, personal pronouns refer to an antecedent proper noun in a sentence. 

As an NLP professional, you will use artificial intelligence models to analyze natural language. 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.

2. Familiarity with text-processing techniques

NLP text processing techniques include: 

• Lemmatization: Removing the endings of similar words to group them by dictionary root (or lemma) 

• Stemming: Removing the endings of words in a way similar to lemmatization but in a way that leaves non-dictionary stems 

• Part-of-speech tagging: Determining what individual words do semantically in a sentence—i.e., whether they’re nouns, verbs, adjectives, etc. 

• Coreference resolution: Identifying when two different words refer to the same semantic entity

• Named entity recognition: Extracting words from a sentence and sorting them into categories such as places, people, time, etc. 

• Word sense disambiguation: Choosing between definitions of a word whose meaning, in the context of a sentence, is ambiguous 

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

3. Knowledge of sentiment analysis

Sentiment analysis involves parsing a customer-composed text to see if its sentiment, or overall emotional tone, is:

• Positive

• Negative

• Neutral

NLP-based AI programs perform sentiment analysis based on keyword analysis: they scan documents for emotion words or phrases—loved, hated, disappointed with—that they categorize as mentioned above. With sentiment analysis, businesses can automatically scan and analyze text types such as emails and customer reviews to determine how much customers like or don’t like a particular product or service. This allows a company to make intelligent business decisions based on consumer preferences. 

NLP-based sentiment analysis techniques not only give a company a picture of how customers feel about their products and services; they also do so: 

• At scale

• Without bias

• In real-time

Different types of sentiment analysis include: 

• Aspect-based: This focuses on specific aspects of a product or service rather than the whole product.

• Emotional detection: Emotional detection attempts to identify the individual's emotional state when writing a review. 

• Fine-grained scoring: This allows for a range of emotional responses (e.g., from very satisfied to very dissatisfied). This is the hallmark of the five-star rating system (rather than just categorizing responses as positive, neutral, or negative). 

• Intent-based: Intent-based sentiment analysis can be used in market research to understand prospective customers' intentions throughout the purchase cycle. 

Technical skills

NLP is a highly complex discipline. As such, you’ll need various skills to utilize it capably. Such skills include: 

4. Proficiency in Python and relevant libraries

NLP professionals need a working knowledge of Python, Java, and Scala. They also need to be proficient with NLP libraries such as:

• NLTK

• spaCy

• Hugging Face 

• Gensim

• PyTorch

• TensorFlow

5. Understanding of machine learning algorithms

ML is at the heart of NLP. To work in NLP, you must be proficient in ML programming methodologies such as: 

• Deep learning: A subset of ML, deep learning teaches AI to “think” in a human-like way. It’s effective with large unstructured data sets and can engage in unsupervised learning.

• Reinforcement learning: Reinforcement learning forgoes labeled correct and incorrect behavior in favor of a trial-and-error approach with a reward function for correct behavior. 

• Supervised learning: This approach trains a model with labeled inputs and outputs, allowing the model to categorize data by common traits.

• Unsupervised learning: A model makes a pattern prediction when fed unlabeled data. 

Analytical skills

While technical skills are important in NLP, you will also need a variety of analytical skills. Examples include data annotation, labeling, and evaluating NLP model performance.

6. Data annotation and labeling

Data annotation and labeling involve adding context and additional explanatory information to an NLP training model’s inputs to facilitate language learning. It often involves the manual labeling of content to help ensure quality data.

7. Evaluating NLP model performance

You’ll need to evaluate your NLP model’s performance continuously. That is, you need to make sure it continues to perform the task for which you designed it. You want to identify issues such as: 

• Data drift: Changes observed in the data set can sometimes but not always be attributed to adding new data. 

• Bias: NLP models trained on large online data sets may pick up biased information. They will learn this without understanding that it’s factually inaccurate or socially harmful and may reproduce it when users request output. 

• Accuracy: You’ll need to fine-tune your model for accuracy continuously. 

NLP programmers use metrics that fall under the headings “classification” and “regression” to precisely evaluate a model's performance. 

Classification metrics measure how well your model classifies and segments information. To do this, you use four primary confusion matrices: 

• True negative: Correctly classified negative cases

• True positive: Correctly classified positive cases

• False negative: Positive cases incorrectly classified as negative 

• False positive: Negative cases incorrectly classified as positive 

This collection of matrices allows you to examine classification metrics such as: 

• Accuracy

• Precision

• Recall

• F1 score

Regression metrics measure overall continuous output, rather than the individual values for which you use classification metrics. Regression metrics are as follows: 

• Coefficient of determination (R-squared)

• Mean squared error

• Mean absolute error

• Mean absolute percentage error 

• Weighted mean absolute percentage error 

Each of these metrics comes to bear when evaluating your model’s overall performance.

Communication skills

After you’ve designed, built, and evaluated your NLP model, you’ll need to be able to explain why you did what you did. Necessary communication skills for NLP professionals include:

8. Explaining NLP concepts to stakeholders

NLP is a complicated subfield of AI. As common as AI is, it is easier to understand for some than others. This is why you must hone your communication skills to convey highly sophisticated information to non-specialist stakeholders. Find some tips below: 

• Work on active listening. Ask questions to clarify what people are asking and respectfully pay close attention to them. 

• Pay attention to communication styles. Some people are active communicators, some are passive communicators, and others are assertive communicators. Understanding how to communicate with a variety of people will be helpful.

• Accept feedback. Allow for a free and open exchange of ideas. 

You’ll also want to make sure you’re adept at the following types of communication: 

• Conflict management

• Cross-cultural communication

• Digital communication

• Empathy

• Public speaking

• Written and verbal communication

9. Collaborating with data professionals

Data scientists help stakeholders make important business decisions based on the analysis of complex data sets. Because so much of NLP is based on data, you may collaborate regularly with data scientists. To facilitate communication, you may want to understand key data science concepts such as: 

• Data cleaning

• Data mining

• Data modeling

• Data warehousing

• Linear algebra

• Statistics

How to gain more NLP skills

To gain more NLP skills, first begin by attaining competence with elemental NLP tools and concepts such as: 

• Python

• Mathematics

• Decision trees

• Naive Bayes

• Logistic regression

• Neural networks

• Frameworks (TensorFlow, PyTorch)

• Data preprocessing techniques

You can learn more by taking online courses, attending boot camps, enrolling in formal classes, and signing up for developing and experienced learners forums.

Start building your natural language processing skills

Building your technical and workplace skills in natural language processing can help you prepare to work in this evolving subfield of AI. Learn more with these resources from Coursera:

• Watch on YouTube: Artificial Intelligence Jobs Unlocked: Pathways to the Future

• Compare programs: Which Introduction to AI Is Best for You? Google vs. IBM vs. DeepLearning.AI

• Enhance your productivity: ChatGPT Prompt Cheat Sheet

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