Types of AI Algorithms and How They Work

What Is Natural Language Processing?

Natural language refers to the regular speech and text that we use to communicate with each other. Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that enables computers to understand, interpret, and generate human language.

NLP bridges the gap between human communication and computer understanding by combining computational linguistics with machine learning, explains Arturo Buzzalino, Chief Innovation Officer, Epicor.

"AI includes other domains besides NLP, such as computer vision which deals with analysis and generation of images, but advances in NLP in the last few years have been at the heart of the current AI revolution," says Stefan Leichenauer, VP of Engineering, SandboxAQ.

You may like

Describing NLP as the analysis and generation of natural language with computers, he says, it is the use of Large Language Models (LLMs) and chatbots that are driving a lot of the excitement around the subject.

NLP and LLMs

Drilling down further, Volodymyr Kubytskyi, Head of AI in MacPaw, says popular LLMs like OpenAI's ChatGPT or Google's BERT, are trained on massive amounts of text data, allowing them to grasp not just individual words but context, nuance, and even creativity in language.

He argues that it is these LLMs that have pushed NLP to new heights, enabling machines to generate coherent, human-like text, summarise long documents, translate between languages, and even engage in meaningful dialogue. By leveraging these models, NLP can now do things that seemed impossible a few years ago, like writing essays or answering complex customer inquiries in a natural, flowing manner.

"LLMs are the engine that's driving much of today's progress in making machines capable of human-like conversations," says Kubytskyi. "This is AI meeting language at an incredibly sophisticated level."

Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed!

Why should businesses care about NLP?

Leichenauer says because natural language is the way we communicate with each other, a lot of our business operations are encoded in natural language.

"Our reports and presentations, our internal memos and emails, and all of our customer communications are written in natural language," says Leichenauer. "NLP techniques can accelerate and automate workflows involving all of these things."

Building on this Buzzalino explains businesses should care about NLP because it allows them to extract meaningful insights from unstructured text data like customer reviews, emails, and social media posts.

NLP, he says, can help automate tasks such as customer support through chatbots, sentiment analysis for market research, and efficient document processing, thereby improving efficiency and enhancing customer engagement.

Sukh Sohal, Senior Consultant at Affinity Reply agrees. He says NLP brings real impact to businesses by transforming how they engage with customers, handle data, and even communicate internally.

"Imagine an AI that can analyze thousands of customer messages in minutes, picking up on common issues, emotions, or trends," says Sohal. "For companies, NLP can be the difference between overwhelming customer service demands and an efficient, responsive operation."

He says NLP lets businesses automate repetitive tasks, improve customer experience, and respond dynamically to feedback while freeing up human teams for tasks that require real insight.

Kubytskyi is excited about the use of LLMs and how it's elevating these NLP capabilities. For instance, he says, customer service bots

"This level of understanding allows businesses to offer personalized, responsive services without sacrificing efficiency," says Kubytskyi.

NLP applications

NLP has become so integrated into our lives that we often overlook it.

Buzzalino points to virtual assistants like Siri and Alexa that understand voice commands, customer service chatbots that handle inquiries, machine translation services like Google Translate, sentiment analysis tools that gauge public opinion on social media, and text analytics systems that extract key information from large volumes of documents, as some real-world applications of NLP.

One real-world application of NLP that strikes Leichenauer is as a smart assistant for writing code. This enables developers to operate more efficiently and also allows for low-code and no-code solutions that are more powerful than before.

How does NLP work?

Unlike traditional computing, which relies on straightforward commands, NLP involves teaching machines to grasp the subtleties and quirks of human language, including context, tone, and meaning, says Sohal. It's how AI moves from rigid rule-following to more intuitive understanding, opening up new ways for tech to interact with us in a more "human" way.

NLP is built on two key components. There's Natural Language Understanding (NLU), which analyses input to extract meaning and intent, and Natural Language Generation (NLG), which produces responses based on context and system logic, says Dan Balaceanu, Co-Founder & Chief Product Officer at DRUID AI.

For example, when a user requests to "book a flight to London," NLU identifies "book" as the action and "London" as the destination, while NLG generates a follow-up response, like "I found a flight to London for £220. Would you like to book it?"

Technically speaking, Sohal says, NLP works by breaking language down into patterns computers can recognize. It starts with tokenization, where sentences are split into words or smaller chunks. Then, grammar and structure are analyzed to understand the relationships between words.

Semantics come next, where computers use massive data to grasp meanings, even for slang or idioms. Finally, context and intent are added through machine learning, especially deep learning. "Here, NLP models learn from large datasets to identify emotions, requests, or subtleties in language, making responses more human-like," says Sohal.

Balaceanu adds this process standardizes vocabulary by reducing words to their root forms and filtering out common words that add little meaning, which helps to identify the real intent of the prompt that it should respond to, and how it should answer.

He adds that to improve the accuracy of the responses, NLP leans on machine learning techniques, such as deep neural networks, and models like transformers such as BERT.

"For NLP systems to respond accurately, they are trained on vast datasets that include diverse language patterns, grammar rules, and sentence structures, covering a range of possible queries and responses," adds Arunkumar Thirunagalingam, Manager, of Enterprise Data Management at Santander Consumer USA.

He says this training involves machine learning models and deep learning techniques that expose the AI to various linguistic scenarios, enabling it to recognize intent, context, and nuances. Over time, and with continuous learning from large, representative datasets, AI systems become more adept at handling complex language tasks and providing relevant, human-like responses.

6 Best NLP Tools: AI Tools For Content Excellence

eWEEK content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.

Natural language processing (NLP) is a subfield of artificial intelligence (AI) that enables computers to interpret and understand human language. Using machine learning (ML) and AI, NLP tools analyze text or speech to identify context, meaning, and patterns, allowing computers to process language like humans do.

One key benefit of NLP is that it enables users to engage with computer systems through regular, conversational language, meaning no advanced computing or coding knowledge is needed. It's the foundation of generative AI systems like ChatGPT, Google Gemini, and Claude, powering their ability to sift through vast amounts of data to extract valuable insights.

I evaluated six top-rated NLP software to see how they compared on features, pros and cons, and pricing.

Featured Partners: AI Software Top natural language processing tools comparison

NLP tools enable computers to understand and interpret human language. The table below compares the best NLP tools based on key criteria, including starting price and use case.

Best for Supported languages Open source License Free plan Grammarly Content creation Multiple (JavaScript, Python, etc.) No Proprietary Yes IBM Watson Comprehensive enterprise text analysis Multiple (Python, JavaScript, C++, etc.) No Proprietary Free for 30,000 NLU items per month Google Cloud Natural Language API Best for scalable text insights Multiple (PHP, Node.Js, Python, etc.) No Proprietary $300 in free credits for new customers spaCy Fast, production-ready text processing Python Yes MIT Yes Hugging Face Transformers Pre-trained models and customizations Python, JavaScript (via API) Yes Apache 2.0 Yes Natural Language Toolkit Educators and researchers Python Yes Apache 2.0 Yes

Overall, I picked Grammarly as the top NLP tool, ranking first for integrations and tying for cost. 

Continue reading to learn more about each platform's use case, pricing, and key features, or skip ahead to see my scoring logic.

Grammarly

Best for AI-powered content creation

Overall rating: 4.2/5

Core features: 3.2/5

Ease of use: 5/5

Integrations: 5/5

Cost: 4/5 

Grammarly is an AI-powered writing assistant that helps you improve your written communication across various platforms. It analyzes texts in real time and identifies potential issues in grammar, spelling, punctuation, and more. It also allows you to generate outlines, draft short stories, and enhance existing texts in various writing contexts.

Grammarly stands out for its intuitive interface and sophisticated writing and editing tool that evaluates overall document quality. You can customize style preferences and voice settings and receive tailored recommendations based on your goals. It also has a built-in plagiarism checker and AI detector, adding another layer of content verification all in one place.

Why I picked Grammarly

Grammarly's features and accessibility are remarkable, catering to a wide range of users and supporting various use cases, from academic writing to professional communication. Its cross-platform functionality ensures writers receive instant feedback and recommendations.

Grammarly's AI content creator also offers advanced generative AI capabilities, making it an ideal choice for producing high-quality text for blog posts, social media, documents, email campaigns, and more.

Pros and cons Pros Cons Real-time grammar and style checking Limited free plan features Seamless integration with various content platforms Occasionally flags stylistic preferences as errors Advanced insights into tone and readability Limited offline functionality Pricing

Free: One user

Premium: $12 per user, per month, billed annually; $21 per user, billed monthly

Enterprise: Contact sales for a quote

Key features

Uses AI to create high-quality content 

Proofreads text, detecting issues in grammar, punctuation, and style

Instantly identifies and adjusts your writing's tone 

Checks for plagiarism and AI-generated content

IBM Watson 

Best for comprehensive enterprise text analysis

Overall rating: 4/5

Core features: 3.3/5

Ease of use: 5/5

Integrations: 3.3/5 

Cost: 5/5 

IBM Watson Natural Language Understanding (NLU) is a cloud-based platform that uses IBM's proprietary artificial intelligence engine to analyze and interpret textual data. It can extract critical information from unstructured text, such as entities, keywords, sentiment, and categories, and identify relationships between concepts for deeper context.

IBM Watson NLU is popular with large enterprises and research institutions and can be used in various applications, from social media monitoring and customer feedback analysis to content categorization and market research. It's well-suited for organizations that perform advanced text analyses to enhance decision-making and gain a deeper understanding of customer behavior, market trends, and other essential data insights. Plagiarism checker and AI detector, adding another layer of content verification all in one place.

Why I picked IBM Watson NLU

IBM Watson NLU impresses with its advanced text analytics capabilities. Its numerous customization options and integration with IBM's cloud services make it a powerful and robust solution for businesses in industries like customer service, healthcare, finance, and human resources. 

Pros and Cons Pros Cons Simple and easy to use Documentation could be better Recognizes semantic roles Sentiment analysis accuracy could be improved Customizable models High-volume use can be costly Pricing

Lite: Free for 30,000 NLU items per month, along with one custom model

Standard: $0.003 per 1–250,000 NLU items; $0.001 per 250,001–5,000,000 NLU items; $0.0002 per anything more than 5,000,000 NLU items

Custom entities and relations model: $800 per custom model per month

Custom classification model: $25 per custom classification model per month

NLU items are units of text up to 10,000 characters analyzed for a single feature; total cost depends on the number of text units and features analyzed.

Key features

Extracts metadata from text, including language, concepts, and semantic roles

Allows creation of custom models with domain-specific training for specialized industries like finance and healthcare

Integrates with IBM's cloud services for scalable, real-time text analysis

Supports entity linking to Knowledge Graphs for more comprehensive data insights

Google Cloud Natural Language API

Best for scalable text insights

Overall rating: 3.8/5

Core features: 3.1/5

Ease of use: 4.6/5

Integrations: 3.3/5 

Cost: 5/5 

Google Cloud Natural Language API is a service provided by Google that helps developers generate insights from unstructured text using machine learning algorithms. The API can analyze text for sentiment, entities, and syntax and separate them into different categories. It also offers entity recognition, sentiment analysis, content classification, and syntax analysis tools.

Google Cloud Natural Language API is widely used by organizations leveraging Google's cloud infrastructure for seamless integration with other Google services. It allows users to build custom ML models using AutoML Natural Language, a tool designed to create high-quality models without requiring extensive knowledge in machine learning, using Google's NLP technology., and other essential data insights. Plagiarism checker and AI detector, adding another layer of content verification all in one place.

Why I picked Google Cloud Natural Language API

I chose Google Cloud Natural Language API for its efficiency in extracting insights from large volumes of textual data. Its integration with Google Cloud services and support for custom machine learning models make it suitable for businesses performing complex, multilingual text analysis, although costs can add up quickly for high-volume tasks.

Pros and cons Pros Cons Multilingual support Initial learning curve Can classify documents in more than 700 predefined categories Can be expensive for organizations with lots of data Integrates with other Google Cloud services Limited customization for specific domain requirements without using AutoML Pricing

Up to $300 in free credits for new customers

Monthly prices per 1,000-character unit vary by amount and feature

See Google Natural Cloud pricing chart for more information.

Key features

Identifies entities and their sentiment within the text

Allows custom text classification using AutoML for domain-specific needs

Analyzes grammatical structure, extracting tokens, sentences, and syntax details

Provides real-time insights from unstructured medical text through Healthcare Natural Language API

spaCy

Best for fast, production-ready text processing

Overall rating: 3.8/5

Core features: 3.5/5

Ease of use: 4.4/5

Integrations: 2.8/5 

Cost: 5/5 

spaCy stands out for its speed and efficiency in text processing, making it an efficient tool for large-scale NLP tasks. Its pre-trained models can perform various NLP tasks out of the box, including tokenization, part-of-speech tagging, and dependency parsing. The ease of use it offers and streamlined API make it a popular choice among developers and researchers working on NLP projects.

spaCy supports more than 75 languages and has 84 trained pipelines for 25 of these languages. It also integrates with modern transformer models like BERT, adding more flexibility for advanced NLP applications.

Why I picked spaCy

I chose spaCy for its speed, efficiency, and comprehensive built-in tools. Its straightforward API, support for over 75 languages, and integration with modern transformer models make it ideal for research and software development projects. Its comprehensive support for transformer-based pipelines sets spaCy apart for fast, production-ready text processing, which facilitates smooth integration with models like BERT, GPT-2, and XLNet. Through this integration, users can achieve high accuracy in NLP tasks and also manage to train one model to do several related tasks simultaneously.

Pros and cons Pros Cons Straightforward and user-friendly API Limited flexibility for customization and fine-tuning Popular in the NLP community due to speed and efficiency Requires some programming knowledge to customize models Comprehensive built-in language tools Limited support for deep language generation tasks Pricing

Free, open-source library for personal and commercial use

Key features

Offers fast and accurate dependency parsing optimized for production environments

Provides pre-trained language models for 25 languages

Uses word vectors to identify word relationships and similarities

Integrates custom models using TensorFlow or PyTorch

Hugging Face Transformers

Best for pre-trained models and customizations

Overall rating: 3.8/5

Core features: 4.3/5

Ease of use: 3.9/5

Integrations: 3.3/5 

Cost: 3.3/5 

Hugging Face Transformers has established itself as a key player in the natural language processing field. It offers an extensive library of pre-trained models that cater to a range of tasks, from text generation to question answering.

Built primarily for Python, Hugging Face Transformers simplifies working with state-of-the-art models like BERT, GPT-2, RoBERTa, and T5. Developers can access these models through the Hugging Face API and integrate them into applications such as chatbots, translation services, virtual assistants, and voice recognition systems.

Hugging Face is known for its user-friendliness, allowing both beginners and advanced users to utilize powerful AI models without deep-diving into the weeds of machine learning. Its hub houses thousands of community-contributed models, including those fine-tuned for specific use cases like sentiment analysis and question answering.

Hugging Face also supports integration with the popular TensorFlow and PyTorch frameworks, bringing even more flexibility to building and deploying custom models.

Why I picked Hugging Face Transformers

I picked Hugging Face Transformers for its huge library of pre-trained models and its flexibility in customization. Its user-friendly interface and support for multiple deep learning frameworks make implementing robust NLP models quick and seamless.

Pros and Cons Pros Cons Extensive model library High resource requirements for larger models Cross-framework compatibility Learning curve for customization Active community and lots of documentation Limited free API access Pricing

Open-source and free to use under the Apache 2.0 license

Inference endpoints (for deploying any ML model): Starts at $0.033 per hour

Pro account: $9 per month

Enterprise hub: $20 per user, per month

Key features

Provides access to pre-trained models like GPT-2, BERT, and T5

Supports custom model training and fine-tuning for specialized use cases

Compatible with multiple deep learning frameworks (TensorFlow, PyTorch)

Natural Language Toolkit

Best for educators and researchers

Overall rating: 3.8/5

Core features: 3.1/5

Ease of use: 4.4/5

Integrations: 3.3/5 

Cost: 5/5 

The Natural Language Toolkit (NLTK) is a Python library designed for various NLP tasks, including tokenization, part-of-speech tagging, parsing, and named entity recognition, providing a comprehensive toolkit for teaching, research, and building NLP applications. NLTK also provides access to more than 50 corpora (large collections of text) and lexicons for use in natural language processing projects.

NLTK is widely used in academia and research, garnering major community support. Its ability to process and analyze massive amounts of data is why it's considered a valuable resource for those working on sentiment analysis, text classification, machine translation, and more.

Why I picked Natural Language Toolkit

I picked NLTK for its free and open-source format and wide range of functionality. It provides educators and researchers with a plethora of NLP tools and access to various text corpora, making academic and research-based NLP tasks easier and more manageable.

Pros and cons Pros Cons Excellent for academic research and NLP education Not ideal for large-scale, production-level tasks Rich community support and documentation NLTK can be complex for beginners Access to 50+ real-world text corpora and lexicons Lacks modern deep learning tools Pricing

Free, open-source, community-driven project

Key features

Available for use on Windows, Mac OS X, and Linux

Provides tokenization and part-of-speech tagging tools for text analysis

Integrates with WordNet for lexical database access

Includes modules for named entity recognition, facilitating the identification of entities in text

How I evaluated the best NLP Tools

I evaluated the top NLP tools based on key criteria, including core features, ease of use, integrations, and pricing. These software solutions were chosen for the benefits they offer, including better data analysis, enhanced customer service, and improved communication between humans and computers. 

Core features (35%): I considered the availability and depth of essential NLP capabilities, including text summarization, sentiment analysis, content generation, and multilingual support. 

Criteria winner: Hugging Face

Ease of use (30%): For this category, I evaluated the quality of the user interface (UI), user experience design (UX), and the comprehensiveness of documentation or knowledge base to ensure accessibility and usability. 

Criteria winner: IBM Watson NLU

Integrations (20%): I assessed each NLP tool based on its integration with popular apps like Google Suite and Microsoft 365, the availability of APIs or language integration capabilities, and compatibility with CMS and CRM platforms.

Criteria winner: Grammarly

Price (20%): For this category, I scored the NLP tools based on the affordability of their subscription plans and the availability of free trials and free versions.

Criteria Winner: Multiple winners

FAQs How do natural language processing tools work?

NLP tools use algorithms and linguistic rules to analyze and interpret human language. They extract meanings, sentiments, and patterns from text data, which can be used for language translation, chatbots, and text summarization tasks.

What are the applications of natural language processing tools?

NLP is a core feature in modern AI models. Applications include sentiment analysis, information retrieval, speech recognition, chatbots, machine translation, text classification, and text summarization.

What is the best NLP software?

There's no single best NLP software solution, as the effectiveness of a tool can vary depending on the specific use case and requirements. Our review captured the best tools for different users and use cases.

Generally speaking, an enterprise business user will need a far more robust NLP solution than an academic researcher.

Is ChatGPT an example of an NLP tool?

Yes and no. ChatGPT is built on natural language processing techniques and uses deep learning to understand and generate human-like text. This allows it to engage in conversations and handle various language tasks. However, its primary purpose is content generation, rather than traditional NLP tasks like sentiment analysis or named entity recognition.

What are the benefits of natural language processing?

NLP makes it easier to automate repetitive tasks, like sorting customer support emails or analyzing feedback. It can quickly scan large amounts of text data to find key insights, helping companies understand customer sentiment or detect trends. NLP also powers virtual assistants like Siri and Alexa, allowing for more natural conversations between humans and computers.

Bottom line: Natural language processing software drives AI

In recent years, NLP has become a core part of modern AI, machine learning, and other business applications. Even existing legacy apps are integrating NLP capabilities into their workflows. Incorporating the best NLP software into your workflows will help you maximize several NLP capabilities, including automation, data extraction, and sentiment analysis.

Investing in the best NLP software can help your business streamline processes, gain insights from unstructured data, and improve customer experience. Take the time to research and evaluate different options to find the tool that best meets your organization's needs. Ultimately, the success of your AI strategy will heavily depend on your NLP solution.

Read eWeek's guide to the top AI companies for a detailed portrait of the AI vendors serving a wide array of business needs.

What Is Artificial Intelligence?

What Is an Annual Report?

What Is Amortization?

What Is an Accredited Investor?

What Are Angel Investors?

What Is Amazon Bedrock?

What Does Average Trade Price Mean?

What Is Asset Management?

What Are Assets Under Management (AUM)?

What Is an Asset?

What Is the Automated Clearing House (ACH)?

What Is AutoGPT?

What Is Augmented Intelligence?

What Is an Audit?

What Is Arbitrage?

What Is an Accountant?

What Is Artificial General Intelligence (AGI)?

What Is Algorithmic Trading?

What Is Annual Percentage Yield (APY)?

What Is Active Stock-Picking?

What Is Accounting Profit?

What Is Accrued Revenue?

What Is Activist Investing?

After-Hours Trading: What It Is and How It Works

What Is Accrued Interest?

What Is Adjusted Gross Income (AGI)?

All About Asset Classes and Investment Diversification

What Is Asset Turnover?

What is Arweave?

What is Algorand (ALGO)?

What Are Altcoins?

What is Ariva (ARV)?

A Complete Beginners Guide to Atomic Swaps

What is Avalanche (AVAX)?

What is Azuki NFT?

What is the After Repair Value (ARV) Formula in Real Estate?

What Is an Acid Test Ratio?

What Are Abnormal Returns?

What Is After-Tax Income?

What Is an Accelerated Share Repurchase?

What Is Accelerated Vesting?

What Does Alpha Mean in Investing?

What Is Attrition in Business?

What Is Average Revenue Per User?

What Is Average Selling Price?

What Are Agency Debentures?

What Is the American Dream?

What Is Anchoring in Investing?

What Is AI Training?

What Is AI Inference?

What is APR (Annual Percentage Rate)?

What Is Anti-ESG Investing?

What Is Affiliate Marketing?

What Is Apple's Walled Garden?

What Is Annual Recurring Revenue (ARR)?

Understanding Asset Retirement Obligations in Financial Terms

What Is an ACH Payment: Process and Benefits

What are Agency Bonds: Definition and Types

Average Indexed Monthly Earnings (AIME): Definition and Overview

What Are Alternative Investments? Guide with Examples

What Are Alpha Investments? Definition, Benefits & Examples

What Are AAA Bonds? Criteria, Examples & Types

What Is the Attention Economy?

What Is Accounts Payable?

What Is Arrearage? Definition and Financial Implications

What Is an Account Executive? Role and Responsibilities

What Is the Acquisition Method?

Accelerated Computing: What It Is and How It Works

Absolute Error: Definition, Formula, Example

Absolute Income: Definition, vs. Relative, Importance

Annualized Return: Overview, Formula, Example

What Are Accumulating Shares?

American Depositary Receipts (ADRs): Definition, Types, Example

---