Natural Language Processing (NLP) is becoming increasingly ubiquitous across many devices with new uses emerging frequently. This article gives a quick look at the fundamentals of NLP, what it’s used for and different techniques that make it possible.
What is NLP?
NLP is the branch of artificial intelligence (AI) that is responsible for developing ways for machines to understand human language. Its development has been driven by the growth in big data, machine learning, computational linguistics, computer science and the desire to have more human to machine interaction.
A basic human-computer interaction with NLP may look like this:
• Human speaks to the computer
• The computer captures audio and converts it to text
• Computer processes “translated” text
• The computer converts it back to audio and “speaks” to human
With advances in technology, NLP is capable of analyzing large volumes of language-based data in a consistent way.
How is NLP Used?
Beyond requesting songs to be played or marked as your favorites, NLP can do much more. Some of the common ways NLP is currently used include:
• Chatbots to automate customer service and ordering
• Improve search results
• Make text processing faster
• Create advertisements
• Provide suggested responses to texts and emails
• Extract information from websites
• Answer complex questions
• Translation and sentiment analysis
At this stage of development, it’s likely that companies have only scratched the surface of what NLP is capable of. As machines and algorithms get more powerful and complex, it’s like that uses of NLP will expand beyond what we can imagine today.
What are Different Techniques of NLP?
While language and communication, in general, rely on syntax, semantics and pragmatics analysis, NLP needs slightly different techniques to carry out its impressive accomplishments.
This technique separates records into different groups or categories based on labels or codes.
Summarization is the process by which NLP can extract a key sentence or develop a short and accurate summary of a longer piece of text.
This technique organizes documents or records within a classification group. It creates clusters within the broader labels.
NLP also relies heavily on extracting data, keywords, keyphrases and other text.
This technique is utilized by search engines frequently as a way to match similar, duplicate or near-duplicate words or phrases. It’s a way NLP can be leveraged to find similarities between different records.
Sentiment analysis is the technique by which NLP can understand the nuance of and emotion behind what a human is trying to say.
This technique helps machines learn and understand contextual clues humans give when they speak.
Does Your Business Need NLP?
Most likely. If you’re not currently using AI in any capacity, it may be time to catch up.
Many functions within your business – sales, marketing, finance, operations, etc. – could benefit from the adoption of AI and natural language processing.