Natural Language Processing Technology Explained
Have you ever asked Siri or Alexa for a traffic update before your morning commute and marveled both at the accuracy of the answer and how articulately it was delivered? Thanks to the power of natural language processing (NLP) and automatic speech recognition (ASR), these digital assistants can analyze and understand your question, decipher your intent, generate an answer, and deliver it to you. Now you can avoid delays and keep your day on track.
Digital assistants are just one everyday application of NLP, a technology that’s changing the way humans communicate with machines. In this article, we’ll dive into the exciting world of NLP, explaining what it is, how it works, and how it’s most commonly used. Let’s get into it.
What is Natural Language Processing (NLP)?
Natural language processing (NLP) is a subset of artificial intelligence (AI) that gives computers the ability to read and understand human language as it is spoken and written. By harnessing the combined power of computer science and linguistics, scientists can create systems capable of processing, analyzing, and extracting meaning from text and speech.
Powered by machine learning and sophisticated deep learning algorithms, these systems have countless real-world applications, from automatic text translation to voice-powered GPS technology to customer service chatbots.
Believe it or not, NLP has existed since the early 1950s, when Georgetown University and IBM first attempted the fully automatic, machine-generated translation of more than 60 Russian sentences to English. NLP has come a long way in the decades since. Thanks to modern computing power, advances in data science, and access to large amounts of data, NLP models grow more accurate with each passing day. In fact, NLP technology is so prevalent in modern society that we often take it for granted. But if you look beyond digital assistants and email filters, it’s a remarkable science — and a remarkably complicated one at that.
How Does Natural Language Processing Work?
NLP is powered by machine learning that processes speech and text data just like it would any other kind of data. These machine learning systems are fed hours and hours of training data so that they can automatically extract, classify, and label different pieces of speech or text in order to make predictions about what comes next. The more data these NLP algorithms receive, the more accurate their analysis and output will be.
Generally speaking, NLP tasks separate language into shorter, fundamental pieces. Basic tasks include:
Tokenization is the first step in natural language processing. It entails breaking down a string of words into smaller units called “tokens.”
Here’s an example: I really love this song! = “I” “really” “love” “this” “song” “!”
Part-of-speech tagging is the process of assigning a part-of-speech category (noun, verb, adjective, conjunction, etc.) to each token. So, if we take the previous example above, it would look like this:
“I”: PRONOUN, “really”: ADVERB, “love”: VERB, “this”: DEMONSTRATIVE, “song”: NOUN, “!”: PUNCTUATION, SENTENCE CLOSER
Breaking down the sentence and assigning a tag helps the machine understand the relationships between individual words and enables it to make assumptions about semantics.
Lemmatization and Stemming
Lemmatization and stemming are text normalization tasks that help prepare text, words, and documents for further processing and analysis. According to Stanford University, the goal of both stemming and lemmatization is to reduce inflectional forms and sometimes derivationally related forms of a word to a common base form. For instance:
- am, are, is be
- car, cars, car’s, cars’ car
This text mapping will produce a result like this:
The boy’s cars are different colors the boy car be differ color
Since words have so many different grammatical forms, NLP uses lemmatization and stemming to reduce words to their root form, making them easier to understand and process.
Stopword removal is the process of removing common words from text so that only unique terms offering the most information are left. It’s essential to remove high-frequency words that offer little semantic value to the text (words like “the,” “to,” “a,” “at,” etc.) because leaving them in will only muddle the analysis.
Word Sense Disambiguation
Word sense disambiguation is the process of determining the meaning of a word, or the “sense,” based on how that word is used in a particular context. Although we rarely think about how the meaning of a word can change completely depending on how it’s used, it’s an absolute must in NLP.
For example, take the word “bass” — a word with two very different “senses”:
- “She can play the bass very well.”
- “Can you turn down the bass in your stereo? It’s shaking the car.”
People know that the first sentence refers to a musical instrument, while the second refers to a low-frequency output. NLP algorithms can decipher the difference between the two and eventually infer meaning based on its training data.
Text classification assigns predefined categories (or “tags”) to unstructured text according to its content. Text classification is particularly useful for sentiment analysis, a technique used to determine whether the language is positive, negative, or neutral. For example, if a piece of text mentions a brand, NLP algorithms can determine how many mentions were positive and how many were negative.
Common Natural Language Processing Applications
Online search engines are the most common examples of NLP. Every time you search the internet, the search engine uses powerful algorithms to generate results based on your keywords and your intent. That’s how the system can serve you results based on related terms and suggest topics — it learns from what you click on. When you select a search result, the system interprets that as a “correct” search, and it uses that information to grow more accurate in the future.
Many email platforms can automatically organize your email inbox into categories like Primary, Social, Promotions, and Spam. That categorization is thanks to keyword extraction, an NLP task in which the machine analyzes the words in the subject lines, associates them with predetermined tags, and then learns to categorize them where they belong. After years and years of training data, these email filters are now incredibly accurate and keep your inbox from becoming a mess.
Customer Service Automation
We can similarly apply NLP to automating manual customer service tasks. Text classification enables companies to tag incoming customer support tickets based on keyword, topic, sentiment, and importance, freeing up valuable time for human customer service representatives. By eliminating these repetitive manual tasks, customer service teams can provide better support to customers and create efficiencies within their processes.
Customers want fast, on-demand service. Enter chatbots, programs that have the ability to simulate human, text-based conversations. Through natural language processing and generation, chatbots can interpret the intent behind what a customer types, identify keywords, and generate a response based on their understanding of the data.
The best of these chatbots can even interpret a customer’s emotions and offer helpful comments. These machines can dramatically reduce wait times for customer service calls, so you can provide faster solutions for your customers while also freeing up your employees to do more complex work.
Automatic Machine Translation
Automatic machine translation has been widely available for years;. However, while automatic translation capabilities have improved since those first experiments at Georgetown in the ‘50s, it still presents challenges. Effective translation must not only be accurate, but also capture the tone and sentiment of the input language. Simply replacing words in one language with words in a different language fails to communicate the desired result.
The Impact of Natural Language Processing
In a few short years, NLP has become one of the most exciting fields within the AI space. It wasn’t long ago that the idea of a computer that understands human speech was reserved for science fiction stories. But thanks to extensive research into computer science, machine learning, and linguistics, machines can now analyze language data to make sense of text and the spoken word.
Here at Rev, NLP and our world-class automatic speech recognition (ASR) engine power our automated transcription service. This service is fast, accurate, and affordable, thanks to tens of thousands of hours of training data from our network of over 60,000 speech-to-text professionals.