Natural language processing (NLP) is more important than ever before as computers become more integrated into our daily lives.

In the past, you’d need technical knowledge to interact with computers. But now, thanks to NLP, computers can understand and decode human language to respond to our verbal and written commands.

NLP refers to a range of methods for processing language using artificial intelligence.

Today’s users expect to be able to speak to their devices, which means devices need to be able to understand and accurately interpret speech patterns — including different languages, accents, slang, and regional terms. But NLP’s utility extends far beyond speech recognition. You’ll find it in chatbots, hiring tools like applicant tracking systems (ATS), email filters, and more.

Many programming languages can be used to conduct NLP, but Python, in particular, has many high-quality NLP libraries that are used extensively in the industry. These tools include language models and functions for analyzing language and finding insights.

Why is natural language processing important?

NLP’s importance can’t be overstated. Over the last decade, it’s grown to power many computing interfaces that make daily life more convenient. It also plays a huge role in accessibility, making it easier for people with physical and cognitive impairments to navigate and interact with their devices.

How NLP works

A wide range of tools are used within NLP, ranging from algorithms for processing and analyzing text to large language models. Still, whether you’re using numerical or text-based data, the first step is always to prepare the data by standardizing it. This makes it possible for the software to analyze it and find patterns.

Preprocessing text

Before any analysis can happen, the source data has to be cleaned up to make it optimal for NLP tools and models. Text preprocessing is the term used for the preparation of this data. Key parts of preprocessing text include:

  • Formatting and Error Correction: The removal of characters, punctuation, or mistakes that could pollute the analysis derived from the text.
  • Tokenization: Breaking inputted text into separate words or sentences.
  • Stop Word Removal: Normalizing text by removing stop words such as articles and prepositions.

All language includes filler words that don’t help determine a statement’s intent, such as “the” or “me.” Removing these words helps focus the analysis or modeling on the words with the most significance or predictive value. You can use libraries like pandas to automate this process to some extent if you’re working with a large dataset.

Parsing Text

Text segmentation, the grouping of text into meaningful units, plays a huge role in a computer’s analysis. This can be achieved by parsing statements to identify speech, verbs, and proper names.

Prioritizing these high-value words (in lieu of considering each word in a given statement) can streamline text processing. For example, by parsing text, an application could identify the proper name “The Empire State Building” and the verb “walking,” which would indicate a query regarding directions to that location on foot.

Language modeling

We train applications to understand our language, speech patterns, and the structure of our commands through a process called language modeling. Language models allow a system to predict which words will be used and in what order they’ll be introduced, improving the accuracy of NLP. Commonly used models include:

  • Unigram or bag-of-words: This model uses a count of each word used to draw conclusions about the statement or command without considering grammar or syntax. The model simply organizes the words in order of most to least used to suggest intent or meaning that can be drawn from analyzing the words used most often.
  • N-gram: More advanced than the bag-of-words model, n-gram considers which words are placed next to each other and how they subsequently impact the meaning of the statement. The n-gram model works best on longer sentences or statements because a wider sample of words results in natural-sounding language and presents a more accurate prediction of what comes next.
  • Neural language models (NLMs): NLMs are based on neural networks and go deeper than bag-of-words or N-gram to offer an analysis that goes beyond simple sentence structure or word usage frequency.

Topic Modeling

Language modeling can help devices process simple commands and straightforward statements, but it becomes harder to use these models as the commands grow longer. This is where topic modeling comes in. Rather than focusing on the order of the words, topic modeling tries to find hidden topics and meanings within a statement.

Unlike language models that count the frequency of each word and use this count to assign importance, topic models prioritize the words that are used less frequently. This kind of topic modeling is known as term frequency-inverse document frequency (TF-IDF).

Another form of topic modeling is called latent Dirichlet allocation (LDA). This model, based on statistical analysis, determines which words are often used in the same context.

NLP Key Issues and Considerations

Working with NLP, programmers and developers are likely to run into issues surrounding privacy and other hot button topics. Collecting data that powers NLP can be seen as invasive, particularly if it’s then shared with (or sold to) third parties.

For example, prediction software can be powered by location-based data, leading users to wonder how much the app developers know about their movements. And some language models contain memory cells with sensitive information that can identify a user if programmers aren’t careful.

How to learn NLP

The possibilities for a career in NLP are only growing as smart devices become more popular — and more complex. Interested in working in this exciting field? Start by learning Python, then jump into our natural language processing courses like:

You can also check out our Data Scientist: Natural Language Processing Specialist career path. Codecademy Data Science Domain Manager Michelle McSweeney says that NLP Specialists hold a unique role compared to other types of Data Scientists. “This is the entry point for artificial intelligence,” she says. “Working with chatbots and taking data science to the next level of what’s possible in this new world of NLP and language and getting computers to act more like humans.”

In our NLP Specialist career path, you’ll gain all the skills you’ll need to launch your new career. You’ll learn programming with SQL and Python, the fundamentals of supervised and unsupervised learning, text preprocessing, language parsing, and more, as you build your own chatbots and other projects you can use to build a portfolio that’ll help you land a job.

Ready to get started? Sign up now!

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