The devices we used to only dream about are now a reality thanks to natural language processing.
Communication using language is an extraordinary skill that distinguishes man from animal and machines. But could we build a machine that replicates our ability to produce and understand language?
To some extent, this skill is already present in applications like Alexa or Siri. Think autocorrect on a smartphone, or spam recognition in electronic mail.
All these solutions use natural language processing (NLP) technology.
But what exactly is NLP and how does it differ from artificial intelligence (AI)? Let’s discuss by looking at the most interesting applications of NLP.
1. NLP is a part of artificial intelligence (AI)
Artificial intelligence (AI) is a broad concept, and natural language processing is a subset of it.
AI algorithms allow machines to analyze and process huge amounts of data 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 is an interdisciplinary field that focuses on language. It combines AI and linguistics, and naturally automates, translates, and generates predictive text.
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 and speech recognition technology often go hand in hand. They both use machine learning and deep learning to effectively acquire, process, and recognize data sets that relate to speech and text.
A major challenge with natural language data, though, is that it needs to be transcribed and annotated with the real meanings of each statement so the machine learning algorithm can embed associations between real and expressed meanings.
That’s why we still need humans for speech transcription – for computers to understand language the way we do, they have to learn languages the way we do.
4. NLP helps you get better search results and filters 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. 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 translation 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.
Forbes notes, “Machine translation is a huge application for NLP that allows us to overcome barriers to communicating with individuals from around the world as well as understand tech manuals and catalogs written in a foreign language. Google Translate is used by 500 million people every day to understand more than 100 world languages.”
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 are obtained faster, and translation becomes more consistent and accurate.
Get to Know Our Natural Language Processing Capabilities
Summa Linguae recently acquired Datamundi, a company that is creating and enriching language data for linguistic research institutes. They are global NLP system builders. They focus on machine translation and communication bots.
With Datamundi’s addition, we’ll help AI get more “intelligent” with the help of experienced linguists, project managers, developers and engineers who work on an online customized platform. We’ll contribute to each step of the NLP production chain: data discovery and collection, text selection and cleaning, test set creation and validation of the NLP output.
Tell us your needs and we will develop unique tools, engage the right people, and find the optimal solutions to match them.
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