As more and more companies adopt artificial intelligence (AI) in a variety of sectors, these AI are inevitably put in positions where they have to interact with human beings. From customer support chatbots to virtual assistants like Amazon’s Alexa, these use cases necessitate teaching an AI how to listen, learn, and understand what humans are saying to it and how to respond.
One method for teaching AI how to communicate with humans is natural language processing (NLP). Sitting at the intersection at AI, computer science, and linguistics, natural language processing’s goal is to create or train a computer capable of not just understanding the literal words humans say but also the contextual implications and nuances found in their language.
As the AI industry has grown in prominence, so too has the NLP industry. A report from Allied Market Research valued the global NLP market at $11.1 billion in 2020, and it is expected to grow to $341.5 billion by 2030. Within that valuation lies a myriad of both promising startups and experienced tech veterans pushing the science further and further.
History of Natural Language Processing
Natural language processing has been part of AI research since the field’s infancy. Alan Turing’s landmark paper Computer Machinery and Intelligence, in which the famous Turing Test was introduced, includes a task requiring the automated interpretation of natural language.
From the 1950s to the 1990s, NLP research largely focused on the symbolic NLP, which attempts to teach computers language contexts through associative logic. Essentially, the AI is given a human-generated knowledge base designed to include the conceptual components of a language and how those components relate to one another.
Using this knowledge base, the AI can then understand the meanings of words in context through IF-THEN logic. An example of this would be similes. If you said, “He’s as fast as a cheetah,” the AI would understand that the person you are talking about would not be a literal cheetah.
Thanks to increases in computing power starting in the 1990s, machine learning algorithms were introduced into natural language processing. This is when machine translation programs started gaining prominence. Examples you might use would be Google Translate or DeepL.
As the internet grew in popularity through the 2000s, NLP machines gained access to even more raw data to sift through and understand. As such, researchers began focusing on developing unsupervised and semi-supervised learning algorithms. These algorithms were less accurate than supervised learning algorithms, but the sheer quantity of data they processed can offset these inaccuracies.
Today, many natural language processing AIs use representational learning and deep neural network-style machine learning techniques to develop more accurate language modeling and parsing capabilities.
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Benefits of Natural Language Processing
Using natural language processing in business has a number of benefits. For instance, NLP programs used in customer support roles can be active 24/7 and can be cheaper to implement and maintain than a human employee. This makes NLP a potential cost-saving measure.
NLP can also be used to nurture leads and develop targeted advertising, ensuring that an organization’s products are being put in front of the eyes of the people most likely to buy them. This can help boost the effectiveness of human marketing teams and drive revenue up without necessarily needing to spend money on more widespread advertising campaigns.
Natural language processing can also be used to boost search engine optimization (SEO) and help make sure a business stays as high in the rankings as possible. NLP can analyze search queries, suggest related keywords, and help save time on SEO research, giving businesses more time to optimize their content quality.
Top Natural Language Processing Companies
One of the biggest names in AI and tech, Google naturally has a long history of utilizing NLP in its products and services. Just this year, one of its researchers asserted that one of the company’s Language Model for Dialogue Applications (LamDA) was sentient, thanks in large part to its responses to the researcher via text chat. Google even began public testing of LamDA in late August 2022.
In terms of product offerings, it has a Natural Language API which allows users to derive new insights from unstructured text. Its AutoML provides custom machine learning models to better analyze, categorize, and assess documents. The Dialogflow development suite can be deployed in a variety of different settings to create conversational user interfaces such as chatbots on websites, mobile apps, and other platforms.
Finally, Google Cloud’s Document AI solution lets customers automate data capture at scale, allowing them to extract more data from documents without boosting costs.
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Automated Insights’ Wordsmith platform is touted as the world’s first publicly-available natural language generation (NLG) engine. By inputting information into the engine, users can create clear, understandable content powered by AI.
Being one of the first of its kind, the platform has a number of interesting clients. Notably, the Associated Press has partnered with Automated Insights to power over 50,000 AI-generated news articles, according to Automated Insight’s website.
Wordsmith’s interface is one of the easiest to use on the market with a high degree of customizability. However, initial setup can take longer than expected. Those looking for quick-deployment options might need to look elsewhere. The content output will also likely need some touching up by in-house staff before publication.
Overall, Wordsmith is a solid choice for companies looking for a way to convert large volumes of data into readable, formatted content.
Based out of Cyprus, Indata Labs leverages its employees’ experience in big data analytics, AI, and NLP to help client companies get the most out of their data. Organizations in industries like healthcare, e-commerce, fintech, and security have made use of Indata Labs’ expertise to generate new insights from their data.
The firm offers a wide range of services and solutions, from data engineering to image recognition to predictive analytics. In the NLP space, the firm offers customer experience consulting, consumer sentiment analysis, and text analysis to ensure clients generate as much value from their datasets as possible.
Indata Labs also maintains its own in-house AI R&D (research and development) Center and works with some of the best computer vision and NLP companies in the world to develop new solutions and push the fields of business intelligence, AI, and natural language processing forward.
Another tech titan, IBM’s suite of Watson AI products are some of the best on the market. Naturally, Watson’s wide array of services features a number of NLP solutions. Watson Discovery is an intelligent search and text analysis platform which enterprises can use to help find information potentially hidden in their vast stores of data.
Watson Assistant is a customer support platform which collects data from client conversations. Through this, Watson Assistant chatbots can better learn how to make the customer support process less stressful and time-consuming for customers.
Finally, Watson Natural Language Understanding uses deep learning to identify linguistic concepts and keywords, perform sentiment analysis, and extract meaning from unstructured data.
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Synthesia is a web-based AI video generation platform. Through its library of video templates, AI voices, and avatars, users can craft videos at-scale to meet whatever needs they might have. Synthesia’s tech has been used by over 10,000 companies, including Nike, Google, the BBC, and Reuters, to create videos in over 60 languages, according to its website.
Other features on the platform include a screen recorder, custom AI avatar crafting, closed captioning, and access to a library of royalty-free background music. If an organization has access to its own library of media assets, they can easily upload and then use these assets in Synthesia.
A major tech name like Intel is bound to have a whole host of NLP-related services. There is, of course, Intel’s wide array of AI products, from development tools to deployment solutions.
For organizations interested in leveling up their NLP knowledge, Intel offers an extensive natural language processing developer course where students can learn the ins and outs of actually utilizing NLP in AI training.
There is also the Natural Language Processing Architect, a Python library developed by the Intel AI Labs. A Python library is, in essence, a collection of premade collections of code which can be repeatedly implemented in different programs in scenarios. The NLP Architect specifically is meant to help make developing custom NLP-trained AI easier.
MindMeld offers a conversational AI platform through which companies can develop conversational interfaces designed to best suit their apps, algorithms, and platforms.
Through MindMeld, companies have developed and deployed interfaces for food ordering, home assistance, banking assistance, and video discovery. It provides training at each step of the NLP hierarchy, ensuring each level of logic in the process is accounted for.
It’s thanks to this innovative platform that Entrepreneur Magazine placed MindMeld in its 100 Brilliant Companies list in 2015. Companies using MindMeld include Cisco, Appspace, Davra, and Altus.
Microsoft’s reach expands across the entire tech landscape. It’s no surprise that AI, and by extension natural language processing, is one area of interest to the Washington-based tech giant. In fact, Microsoft’s Research Lab in Redmond, Washington, has a group dedicated specifically to NLP research.
Through Microsoft’s Azure cloud computing service, customers can train and deploy customized natural language processing frameworks. The company even offers documentation on how to do so. To utilize NLP in Azure, Microsoft recommends Apache Spark, an open-source unified analytics engine built for large-scale data processing.
Notable features of these customized NLP frameworks for Azure include sentiment analysis, text classification, text summarization, and embedding. Additionally, Microsoft’s Azure AI can support a multilingual training model, allowing organizations to train NLP AI to perform in multiple different languages without retraining.
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