Best Natural Language Processing Companies 2022 | eWEEK

As more and more companies adopt Artificial Intelligence (AI) in a variety of sectors, these AIs are inevitably placed in positions where they have to interact with humans. From customer support chatbots to virtual assistants like Amazon’s Alexa, these use cases require teaching AI how to listen, learn, and understand what humans are saying and how to respond.

One way to teach AI how to communicate with humans is natural language processing (NLP). At the intersection of artificial intelligence, computer science, and linguistics, the goal of natural language processing is to create or train a computer capable of not only understanding the literal words humans say but also the contextual implications and nuances that exist in their language.

With the growth of the AI ​​industry, the NLP industry has also grown. report from Allied Market Research The global NLP market was valued at $11.1 billion in 2020, and is expected to grow to $341.5 billion by 2030. Within this valuation lie countless promising startups and experienced tech professionals who are pushing the science even further. .

History of Natural Language Processing

Natural language processing has been a part of artificial intelligence research since the field’s inception. Alan Turing Historical Paper Computer machines and intelligencein which the famous Turing Test is presented, includes a task that requires automated natural language interpretation.

From the 1950s to the 1990s, NLP research focused heavily on Symbolic NLP, which attempts to teach computer language contexts through associative logic. Essentially, AI is given a man-made knowledge base designed to include the conceptual components of language and how those components relate to each other.

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 say, “He’s as fast as a cheetah,” the AI ​​will understand that the person you’re talking about won’t be a literal cheetah.

Thanks to the increases in computing power that began in the 1990s, machine learning algorithms have been introduced into natural language processing. This is when machine translation software started gaining prominence. Examples you might use would be Google Translate or DeepL.

As the Internet became more and more popular during the 2000s, NLP machines gained access to more raw data for browsing and understanding. As such, researchers have begun to focus on developing unsupervised and semi-supervised learning algorithms. These algorithms were less accurate than supervised learning algorithms, but the huge amount of data processed could compensate for these errors.

Today, many natural language processing AI systems use representational learning and deep neural network-style machine learning techniques to develop more accurate language analysis and modeling capabilities.

Read more on: What is artificial intelligence?

Benefits of natural language processing

Using natural language processing in business has a number of benefits. For example, 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 Neuro Linguistic Programming (NLP) a potential cost-saving measure.

Neuro Linguistic Programming (NLP) can also be used to nurture leads and develop targeted advertising, ensuring that an organization’s products are placed before the eyes of the people most likely to buy them. This can help enhance the effectiveness of human marketing teams and increase revenue without necessarily spending money on more widespread advertising campaigns.

Natural language processing can also be used to boost search engine optimization (SEO) and help ensure that businesses stay in the top rankings as much as possible. Neuro Linguistic Programming (NLP) can analyze search queries, suggest relevant keywords, and help save time on SEO research, giving companies more time to improve the quality of content.

Best natural language processing company

The Google

One of the biggest names in artificial intelligence and technology, The Google Naturally it has a long history of using NLP in its products and services. Only this year, one of its researchers confirmed That one of the company’s language models for Dialogue Applications (LamDA) was conscious, thanks in large part to his responses to the researcher via text chat. google up LamD public testing has beguna In late August 2022.

In terms of product offerings, it has a natural language API that allows users to extract new insights from unstructured text. Its AutoML provides customized machine learning models to better analyze, classify, and evaluate documents. The Dialogflow development kit 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 allows customers to automate data capture at scale, allowing them to extract more data from documents without increasing costs.

Read more on: The future of artificial intelligence


machine visionsThe Wordsmith platform is touted as the world’s first publicly available Natural Language Generation (NLG) engine. By entering information into the engine, users can create clear and understandable content powered by artificial intelligence.

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 more than 50,000 news articles generated by artificial intelligence, according to Automated Insight website.

Wordsmith’s interface is one of the easiest to use on the market with a high degree of customization. However, the initial setup may take longer than expected. Those looking for quick publishing options may need to look elsewhere. It’s also possible that the output of the content will need some touching by internal staff before publishing.

Overall, Wordsmith is a solid choice for companies looking for a way to convert large amounts of data into rich, readable content.

Indata Labs

based out of Cyprus, Indata Labs It leverages its employees’ expertise in big data analytics, artificial intelligence, and natural language processing to help client companies get the most out of their data. Organizations in industries such as healthcare, e-commerce, financial technology, and security have leveraged Indata Labs’ expertise to generate new insights from their data.

The company offers a wide range of services and solutions, from data engineering to image recognition to predictive analytics. In NLP, the company offers customer experience consulting, consumer sentiment analysis, and text analysis to ensure that customers generate as much value as possible from their data sets.

Indata Labs also maintains its own 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 advance the fields of business intelligence, artificial intelligence, and natural language processing.


Another tech giant, IBM The range of Watson AI products are among the best on the market. Naturally, Watson’s wide range of services features a number of NLP solutions. Watson Discovery is a smart text-research and analysis platform that organizations can use to help find potentially hidden information in their massive data stores.

Watson Assistant is a customer support platform that collects data from customer conversations. Through this, Watson Assistant chat bots 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 language concepts and keywords, perform sentiment analysis, and extract meaning from unstructured data.

Read more on: Benefits of artificial intelligence


Cynthia It is a web-based AI video generation platform. With its library of video templates, AI voices, and avatars, users can create videos at scale to meet any needs they may have. Synthesia technology has been used by more than 10,000 companies, including Nike, Google, BBC and Reuters, to create videos in more than 60 languages, according to its website.

Other features on the platform include a screen recorder, custom AI avatar crafting, closed captions, and access to a royalty-free background music library. If an organization has access to its own library of media assets, it can easily upload those assets and then use them in Synthesia.

Intel Corporation

A major technical name like Intel Corporation It must have a full range of NLP related services. There is of course a wide range of AI products from Intel, from development tools to deployment solutions.

For organizations interested in improving their level of knowledge in NLP, Intel offers a crash course for NLP developers where students can learn the ins and outs of using NLP in AI training.

There is, too Natural Language Processing EngineerPython library developed by Intel AI Labs. A Python library is, at its core, a set of pre-made sets of code that can be implemented recursively in different programs in different scenarios. An NLP engineer specifically aims to help facilitate the development of AI trained in NLP.

mind mix

mind mix It offers a conversational AI platform through which companies can develop conversational interfaces designed to better suit their applications, algorithms, and platforms.

Through MindMeld, companies have developed and published interfaces for ordering food, home help, banking assistance, and video discovery. It provides training at every step of the NLP hierarchy, ensuring that every level of logic is accounted for in the process.

Thanks to this innovative platform, Entrepreneur Magazine placed MindMeld in their list of 100 Great Companies in 2015. Companies using MindMeld include Cisco, Appspace, Davra, and Altus.


Microsoft Access is expanding across the entire technology landscape. Not surprisingly, artificial intelligence, and thus natural language processing, is one area of ​​interest for the Washington-based tech giant. In fact, the Microsoft Research Lab in Redmond, Washington, has a group specifically dedicated to NLP research.

With Microsoft’s Azure cloud computing service, customers can train and deploy custom natural language processing frameworks. The company offers up documentation about how to do it. For NLP use in Azure, Microsoft recommends Apache Spark, an open source unified analytics engine designed for large-scale data processing.

Notable features of these custom NLP frameworks for Azure include sentiment analysis, text categorization, text summarization, and embedding. In addition, Microsoft’s Azure AI can support a multilingual training model, allowing organizations to train NLP AIs to perform in several different languages ​​without retraining.

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