Popular science fiction films have long fed us stories of machines and robots that understand what we’re saying. Over the past decade, such communication has become closer and closer to reality thanks to natural language processing technology (NLP). What is NLP and how does it differ from artificial intelligence? We take a look at the most interesting applications of NLP.
What is natural language processing technology?
Communication using language is an extraordinary skill that distinguishes man from other species as well as machines. But could we build a machine that replicates our ability to produce and understand language?
To some extent, this skill is already possessed by applications like Alexa or Siri, autocorrect on a smartphone, or spam recognition in electronic mail. All these solutions use natural language processing (NLP) technology.
Let’s explore the most important aspects of NLP:
1. NLP is a part of artificial intelligence
Artificial intelligence (AI) is a broad concept, and natural language processing is just a subset of it. AI algorithms allow machines to analyze and process huge amounts of data in order to detect patterns in the data, and therefore, learn something. In some ways, AI allows a machine to “think” in a similar way to the human brain.
2. NLP focuses on language
NLP itself is an interdisciplinary field focused on language. It combines AI and linguistics, which allows for automation, translation, and the generation of text in a similar fashion to how natural language is produced.
Voice assistants or customer service chatbots are perfect examples of NLP in action.
In the past, NLP was based on applying rules-based methods born out of computational linguistics. Over time, its evolved more into a practice about machine learning and AI.
3. NLP can help in speech recognition
Natural language processing is often equated with speech recognition technology. This assumption is correct, since it uses both machine learning and deep learning to effectively acquire, process, and recognize data sets that relate to speech and text.
4. NLP helps you get better search results andfilters content.
NLP solutions are also useful in producing better search results, as recognition and categorization of natural language is crucial.
For example, NLP can be used for customer service, where inquiries can be automatically routed to specific page categories or departments to speed up the response process.
It’s also helpful in e-commerce, where a store’s search engine can give the consumer the product most relevant to their query.
5. NLP helps to reduce language barriers
Natural language processing is also a great support for machine translation. Thanks to NLP and neural engines, automatic translations are more understandable and similar to those performed by humans.
This also means that barriers to international communication are diminishing, because almost everyone in the world today has access to fast translation—even on a smartphone.
In turn, niche, professional translation that relate to specific areas of business, law, or technical materials can be fast-tracked thanks to support from automated translation systems. Thanks to NLP, results can be obtained faster, and translation becomes more consistent and accurate.