What is Natural Language Processing (NLP)?

Natural Language Processing, or NLP, focuses on interactions between computers and human languages. NLP is a field that brings together computer science, artificial intelligence, and linguistics.

Computers are great at handling structured data such as database tables and spreadsheets. But human language is incredibly diverse and complex, and often far from tightly-structured. Human language spans across hundreds of languages and dialects, with large sets of grammar rules, syntaxes, terms, and slang.

Homonyms, or words that are spelled and sound the same but carry different meanings, create an interesting NLP problem. "Paris Hilton listens to Paris Hilton at the Paris Hilton" is a sentence that native English speakers don't have too much trouble parsing but creates a complicated NLP problem. When does "Paris" refer to a person, and when does it signify a hotel's location in France?

Natural Language Processing allows computers to communicate with humans in their own language by pulling meaningful data from loosely-structured text or speech. NLP helps scale language-related tasks. This is what makes it possible for computers to read text (or hear speech), interpret that text or speech, and determine what to do with the information.

NLP helps to resolve ambiguity in language by adding numeric structure to large datasets. This structure makes speech recognition and text analytics possible.

The field of NLP has grown rapidly in the last decade. Thanks to advancements in the field of natural language processing and technologies built on it, someone can now say to a device in their home, "Hey Google, play Never Gonna Give You Up" and hear their favorite song played back to them.

How does Natural Language Processing (NLP) work?

At its core, NLP helps computers understand and even interact with human speech. Natural language processing relies on techniques ranging from statistical machine learning methods to various algorithmic approaches.

Due to the natural variances in human speech, voice and text-based data quality vary widely. This broad spectrum of approaches leveraged by NLP allows for a wide range of applications.

Natural Language Processing tasks serve to:

Break language into shorter pieces of data.
Build an understanding of the various data.
Explore how the components of language work together to create meaning.

You may have dabbled in natural language processing yourself if you ever had to diagram a sentence in school. Tagging various elements of speech, detecting which language is being spoken or written, or identifying semantic relationships between words are all core NLP tasks.

Content categorization, topic modeling, sentiment analysis, speech-to-text transcription, and text-to-speech conversion all leverage these core NLP tasks.

What about semantic analysis?

Semantic analysis is closely related to NLP and helps form the backbone of how computers process human language.

At its core, semantic analysis helps connect a specific word or set of words to contextual meaning. This is what allows humans to understand our "Paris Hilton" example above. A computer needs to leverage semantic analysis to determine if "Paris" refers to a human's name, an artist's catalog, or a city in France.

How does Twilio leverage NLP?

Twilio provides speech recognition, which leverages Natural Language Processing to convert speech to text in real-time during a phone call. This allows your system to analyze meaning and intent.

Twilio has also built out a robust Natural Language Understanding (NLU) engine that powers Understand. This engine turns text from phone calls and messages into structured data that your virtual assistant or other applications can leverage.

With Twilio Understand, you can pivot from DTMF-powered phone tree menus to conversational interactions between your system and your users. Learn more about Understand in this blog post.

Additional information

Natural Language Processing is a vast and complex field of study. You can learn more about NLP through these resources:

Enroll in an online course that teaches Natural Language Processing, like this one from Coursera.
Learn how to apply sentiment analysis to messy data sets
Improve your SMS interactions with Python's TextBlob in this blog post