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What is Natural Language Processing (NLP)?

November 22, 2022

We humans can speak various languages like English, Filipino, Korean, and more. Computers, however, have a vastly different language incomprehensible to most humans.

Natural Language Processing, or NLP, aims to break the communication barrier between humans and machines.

Natural language processing refers to the subfield of computer science focusing on making computers capable of comprehending written and spoken language in a manner that is comparable to that of humans.

Although natural language processing is not a new field of study, it is a rapidly developing technology. This is due to the availability of big data, powerful computing, improved algorithms, and increased interest in human-to-machine communications.

Computer programs that translate text from one language to another, respond to spoken commands, and quickly summarize large text volumes—even in real-time—are powered by NLP. Likely, you have already encountered NLPs in the form of customer service chatbots, speech-to-text dictation software, and voice-operated programs like Siri or Alexa.

Let's take a deeper look into what NLP is, how it can be applied to everyday life, and what we can expect from NLP in the future.

Where did NLP come from?

Natural Language Processing is a fairly old technology that began in the 1950s. Back then, NLP was known as Machine Translation (MT). During World War II, MT was used in codebreaking the Russian language into English. Ultimately, MT failed in this endeavor.

A decade later, early-stage NLP succeeded in developing ELIZA, an Artificial Intelligence (AI) chatbot used for limited conversation in psychological counseling sessions. From then on, NLP has seen continuous development into what we know it today.

From then on, NLP has seen continuous development into what we know it today.

What are NLP's applications?

The field of NLP has three parts:

  1. Speech Recognition — Translation of spoken language into computer language. This can be observed in speech recognition or speech-to-text applications.
  2. Natural Language Understanding (NLU) — Computer analysis and comprehension of the translated computer language. NLU is the most challenging process because the machine should understand what you said. Still, NLU system developers encode rules into their systems and train them to apply them correctly.
  3. Natural Language Generation (NLG) — converts a computer language into text and can also convert that text into audible speech. NLG systems can construct complete sentences with grammar rules and a lexicon.

From these three parts, the following applications have been developed with Natural Language Processing:

Chatbots: Some of the most common NLP applications are Siri Services or Google Search voice commands. Making search engines comprehend the meaning of our questions and producing natural language in response to provide us with answers is a major application of NLP.

Many businesses (knowingly or unknowingly) utilize NLP for lead generation, customer service, taking in orders, and feedback.

Spam Filters: Gmail and other email service providers utilize NLP to distinguish between legitimate emails and spam. Spam filters decipher the meaning of the text in emails you receive to determine whether or not they are spam.

Trading: Algorithmic trading uses NLP to predict stock market conditions. This is done by looking at news headlines about companies and stocks and trying to understand what they mean to determine whether you should buy, sell, or hold.

Info summarization: Much information is available online, and most come from lengthy articles or documents. NLP is used to figure out what the data means and gives shorter summaries so people can understand it faster.

What does the future hold for NLP?

Currently, NLPs need to be developed more to translate complete sentences from one language to another that are grammatically correct.

Natural Language Processing has transformed how we interact with computers. The AI-powered NLP algorithms have replaced the once-complex handwritten formulas manually input in older machines. As NLP shapes and improves communication technology, the technology will drive the shift from data-driven to intelligence-driven endeavors.

Currently, NLPs need to be developed more to translate complete sentences from one language to another that are grammatically correct. For now, businesses are reaping the benefits of NLPs in e-commerce to streamline processes such as lead generation and customer interaction for reservations and other services.

Know more about the different AI services powered by Natural Language Processing on aiah.ai