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6 Best NLP Tools: AI Tools For Content Excellence
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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 comparisonNLP 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 YesOverall, 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.
GrammarlyBest for AI-powered content creation
Overall rating: 4.2/5Grammarly 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.
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 PricingBest for comprehensive enterprise text analysis
Overall rating: 4/5IBM 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.
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 PricingNLU 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 featuresBest for scalable text insights
Overall rating: 3.8/5Google 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.
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 PricingSee Google Natural Cloud pricing chart for more information.
Key featuresBest for fast, production-ready text processing
Overall rating: 3.8/5spaCy 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.
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 PricingBest for pre-trained models and customizations
Overall rating: 3.8/5Hugging 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.
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 PricingBest for educators and researchers
Overall rating: 3.8/5The 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.
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 PricingI 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.
Criteria winner: Hugging Face
Criteria winner: IBM Watson NLU
Criteria winner: Grammarly
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 AIIn 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.
AI For The Rest Of Us: 5 Beginner Tools That Will Actually Make Your Life Easier
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AI productivity tools use artificial intelligence to automate tasks, streamline workflows, and boost efficiency, saving you time and enhancing overall productivity. They can help you get more tasks done faster with less effort, even if you're new to AI or not tech savvy. Whether you want help writing emails, organizing tasks, or summarizing your meetings, there's an AI tool to free up your time for more meaningful and creative work — or for just lounging around by the pool, if that's the way you roll. No judgment here.
Understanding how to use AI productivity tools starts with identifying what they can do for you. Generative AI apps can help you brainstorm fresh ideas or help you draft emails quickly, for example, while natural language processing (NLP) tools can analyze text and extract key information from documents. But you don't need to get hung up on the technical details.
Here is my breakdown of five AI productivity tools you can use to make your life easier.
ChatGPTOpenAI's largest and most popular AI product transformed conversational AI by being able to understand and produce human-like text. This AI productivity tool is great for brainstorming, drafting emails, and outlining reports. With its chatbot-style interface, this AI productivity tool feels as natural as chatting with a friend — which it can totally do if you activate the voice mode.
GrammarlyThis leading AI writing tool is popular for its real-time grammar, spell-checking, tone adjustment, and generative AI features. You feed it prompts, or instructions, and it can create anything from outlines to effective emails based on your guidance. Whether you need an AI tool to help you edit your academic paper, a letter to the editor, a thank you note to a neighbor, a resume, or Harry Potter fan fiction, it can improve the clarity of your work as you write.
Notion AIIf you love keeping your notes and projects organized, Notion AI is a game-changer. This AI productivity tool uses an intuitive, colorful, and customizable platform to help you keep track of your daily tasks and progress toward your goals. Think of it as an all-in-one digital planner that helps you manage your goals, whether you're planning a team project or tracking a simple habit. It's also a great way to collect everything from document summaries to favorite articles to song lyrics to digital copies of your grocery receipts, and more.
Fireflies.AiFireflies.Ai is an AI-powered meeting assistant that lets you easily transcribe, summarize, record, and analyze virtual meetings. It captures video and audio recordings and generates notes so you can read, listen to, or share them. If you want to analyze a very long meeting, this AI productivity tool can help you automatically take notes, generate summaries, and even suggest action items. You can also integrate with conferencing platforms, calendars, and communication tools, which allows you to easily record and summarize your online meetings. This is great for work, but you can also use it for your virtual book club meetings, school reunion planning, or your fantasy baseball league.
CalendlyScheduling meetings for different clients or teams doesn't need to be a hassle. Calendly automates the process by syncing with your calendar and letting others pick times that work for everyone without back-and-forth emails. If you don't have a fixed schedule for meetings or booking appointments, you can use this AI productivity tool to refine your weekly scheduling by offering additional meeting slots or reserving certain slots, like setting up available meeting hours for different days.
Quick tips to get started with AI productivity toolsNot sure where to begin? Here are three tips to make sure you get off on the right foot.
Start with one AI toolPick one AI tool that you think might be both helpful and fun — that will make it less intimidating. After you get comfortable, you can gradually start exploring additional tools to address other tasks that are part of your daily routine. For example, after becoming more confident in using ChatGPT for drafting emails, you can use Grammarly to polish your writing or Calendly to automate scheduling. This approach prevents burnout and allows you to see the benefits of each AI tool before moving on to more advanced features or platforms.
Build your AI skills graduallyYou don't need to be an expert from day one — think of building your AI skills like learning to cook. Start with toast before tackling soufflés. Begin by using AI tools for simple, familiar tasks, like drafting an email or organizing your to-do list. As you get more comfortable, try using AI to brainstorm ideas, summarize long documents, or even suggest creative headlines. The more you experiment, the more natural it becomes. Just like muscle memory, you'll start instinctively knowing which tool to reach for when a task pops up. Before you know it, using AI will feel as routine as checking your inbox.
Track your progressAfter trying different AI tools, you can start monitoring which tasks become faster and easier. Keeping an eye on how AI productivity tools make your life easier and save more time will help you make smarter choices on which tools to keep or adjust. Start by tracking the time you spend on specific tasks before and after using AI. For instance, note how long it takes to transcribe meetings and analyze documents, then compare that with your time after using an AI tool.
Embrace AI as your everyday advantageMany professionals — myself included — have found that using AI tools for everyday tasks at home, school, and work can free up time. How you choose to spend that time is up to you. By using just a few AI productivity tools, you could reclaim at least two hours every workday for more meaningful tasks, creative thinking, or simply enjoying a longer lunch break. As a beginner, you don't have to master these tools right away, but start small, experiment, and find the best tools that will help make your everyday tasks more manageable and efficient.
Do you want to learn how to craft effective prompts for different AI tools without the financial commitment? See our list of the top free, beginner-friendly prompt engineering courses.
3 Open Source NLP Tools For Data Extraction
Unstructured text and data are like gold for business applications and the company bottom line, but where to start? Here are three tools worth a look.
Developers and data scientists use generative AI and large language models (LLMs) to query volumes of documents and unstructured data. Open source LLMs, including Dolly 2.0, EleutherAI Pythia, Meta AI LLaMa, StabilityLM, and others, are all starting points for experimenting with artificial intelligence that accepts natural language prompts and generates summarized responses.
"Text as a source of knowledge and information is fundamental, yet there aren't any end-to-end solutions that tame the complexity in handling text," says Brian Platz, CEO and co-founder of Fluree. "While most organizations have wrangled structured or semi-structured data into a centralized data platform, unstructured data remains forgotten and underleveraged."
If your organization and team aren't experimenting with natural language processing (NLP) capabilities, you're probably lagging behind competitors in your industry. In the 2023 Expert NLP Survey Report, 77% of organizations said they planned to increase spending on NLP, and 54% said their time-to-production was a top return-on-investment (ROI) metric for successful NLP projects.
Use cases for NLPIf you have a corpus of unstructured data and text, some of the most common business needs include
Sometimes, having NLP capabilities bundled into a platform or application is desirable. For example, LLMs support asking questions; AI search engines enable searches and recommendations; and chatbots support interactions. Other times, it's optimal to use NLP tools to extract information and enrich unstructured documents and text.
Let's look at three popular open source NLP tools that developers and data scientists are using to perform discovery on unstructured documents and develop production-ready NLP processing engines.
The Natural Language Toolkit (NLTK), released in 2001, is one of the older and more popular NLP Python libraries. NLTK boasts more than 11.8 thousand stars on GitHub and lists over 100 trained models.
"I think the most important tool for NLP is by far Natural Language Toolkit, which is licensed under Apache 2.0," says Steven Devoe, director of data and analytics at SPR. "In all data science projects, the processing and cleaning of the data to be used by algorithms is a huge proportion of the time and effort, which is particularly true with natural language processing. NLTK accelerates a lot of that work, such as stemming, lemmatization, tagging, removing stop words, and embedding word vectors across multiple written languages to make the text more easily interpreted by the algorithms."
NLTK's benefits stem from its endurance, with many examples for developers new to NLP, such as this beginner's hands-on guide and this more comprehensive overview. Anyone learning NLP techniques may want to try this library first, as it provides simple ways to experiment with basic techniques such as tokenization, stemming, and chunking.
spaCyspaCy is a newer library, with its version 1.0 released in 2016. SpaCy supports over 72 languages and publishes its performance benchmarks, and it has amassed more than 25,000 stars on GitHub.
"spaCy is a free, open-source Python library providing advanced capabilities to conduct natural language processing on large volumes of text at high speed," says Nikolay Manchev, head of data science, EMEA, at Domino Data Lab. "With spaCy, a user can build models and production applications that underpin document analysis, chatbot capabilities, and all other forms of text analysis. Today, the spaCy framework is one of Python's most popular natural language libraries for industry use cases such as extracting keywords, entities, and knowledge from text."
Tutorials for spaCy show similar capabilities to NLTK, including named entity recognition and part-of-speech (POS) tagging. One advantage is that spaCy returns document objects and supports word vectors, which can give developers more flexibility for performing additional post-NLP data processing and text analytics.
Spark NLPIf you already use Apache Spark and have its infrastructure configured, then Spark NLP may be one of the faster paths to begin experimenting with natural language processing. Spark NLP has several installation options, including AWS, Azure Databricks, and Docker.
"Spark NLP is a widely used open-source natural language processing library that enables businesses to extract information and answers from free-text documents with state-of-the-art accuracy," says David Talby, CTO of John Snow Labs. "This enables everything from extracting relevant health information that only exists in clinical notes, to identifying hate speech or fake news on social media, to summarizing legal agreements and financial news.
Spark NLP's differentiators may be its healthcare, finance, and legal domain language models. These commercial products come with pre-trained models to identify drug names and dosages in healthcare, financial entity recognition such as stock tickers, and legal knowledge graphs of company names and officers.
Talby says Spark NLP can help organizations minimize the upfront training in developing models. "The free and open source library comes with more than 11,000 pre-trained models plus the ability to reuse, train, tune, and scale them easily," he says.
Best practices for experimenting with NLPEarlier in my career, I had the opportunity to oversee the development of several SaaS products built using NLP capabilities. My first NLP was an SaaS platform to search newspaper classified advertisements, including searching cars, jobs, and real estate. I then led developing NLPs for extracting information from commercial construction documents, including building specifications and blueprints.
When starting NLP in a new area, I advise the following:
You may find that the NLP tools used to discover and experiment with new document types will aid in defining requirements. Then, expand the review of NLP technologies to include open source and commercial options, as building and supporting production-ready NLP data pipelines can get expensive. With LLMs in the news and gaining interest, underinvesting in NLP capabilities is one way to fall behind competitors. Fortunately, you can start with one of the open source tools introduced here and build your NLP data pipeline to fit your budget and requirements.
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