Top 12 AI Tools for NLP (Natural Language Processing ): 2024

Natural Language Processing (NLP) is a form of computation concerned with free AI Tools for NLP whereby any form of signal, statistics, or machine learning program from human language combines them into text or voice data.

AI Tools for NLP perform a set of functionalities such as processing data on its own and understanding the context with the generation of data as well. It is a collection of linguistic data, breaking down texts into readable forms or tokens by assigning grammatical tokens and thus performing a running analysis.

There are certain AI Tools for NLP to perform such tasks which we are discussing in this article best AI Tools for NLP along with their features, pros & cons, etc.

AI’s Evolution in NLP Tools

AI tools work as Natural Language Processing Tools and it has a rapid growth in this field. In the early 1950s, these systems were introduced and certain linguistic rules were formed but had very limited features. It advanced in the year 2000 when various new models were introduced and the Hidden Markov Model was one of them, which allowed the NLP system. They were known for their analytical power with automatic learning patterns.

It continued to be supervised as Support Vector Machines were launched. They became fairer with Large Language models in 2019. With deep learning sequence tasks applied, in 2020 multimodal was introduced to incorporate new features in a holistic approach marking AI’s Evolution in NLP Tools.

Top 12 AI Tools for NLP (Natural Language Processing)

AI tools for NLP play a key role in language translation, text summarization, fraud detection, searching certain information, speed detection, and answering certain questions as well. They have been a great help to the forward generation for authentic information. Let us look into the Top 12 AI tools for Natural Language Processing in 2024 and their features:

1. MonkeyLearn

MonkeyLearn is considered as a solution that helps a person to extract data that are inside any Gmail, tweets, or from any sentence that is in written form. The extracted data is further converted into visualization which is to be presented to the user for picture-directed work.

Features:

• MonkeyLearn has a graphic user interface.
• The model is commonly used in sentiment analysis and classification of topics.
• Doesn’t require any need for coding or data science knowledge.

Pros:

• The platform provides its tutorial on “how to use” which is beneficial.
• Sample templates for Zoom and Netflix are provided.

Cons:

• The platform is however limited to a certain amount of functions.
• The decision in the classification of emotions is not very enhanced.

Pricing:

• $299 per month considering all the features.

Link: https://monkeylearn.com/

2. spaCY

The most famous AI tool for NLP is spaCY is considered an open-source library that helps in natural language processing in Python. This platform helps in the extraction of information and provides it for NLP which is written in Python.

Features:

• The platform converts segmented text into words, punctuation marks, etc.
• The ability to compare words, text spans, and documents and check similarity.
• Labeling syntactic dependencies involves assigning labels.

Pros:

• The platform simplifies natural language processing.
• It is easy to use and the extraction of insight is accurate and quick.

Cons:

• The platform is considered less flexible lacking pre-trained models.
• The speed of extraction of insights is particularly slower.

Pricing:

• The platform is an open-source library, hence free to use.

Link: https://spacy.io/

3. Stanford CoreNLP

Stanford CoreNLP is a type of backup download page that is also used in language analysis tools in Java. It takes the raw input of human language and analyzes the data into different sentences in terms of phrases or dependencies.

Features:

• An artificial shop for all the natural languages in Java.
• The platform also enables the users to derive linguistic annotations.
• The platform can also identify the parts of speech in the sentence.

Pros:

• The platform can be customized by the user in any way the process is proposed.
• Supports 8 natural languages such as Arabic, Chinese, English, French, German, Hungarian, Italian, and Spanish.

Cons:

• The default command in opening of the platform is slow.
• Depiction and translation of language has lots of bugs.

Pricing:

• As an open source, it can be directly downloaded in zip for free.

Link: https://stanfordnlp.github.io/CoreNLP/

4. MindMeld

MindMeld is considered a language conversation platform that assists in having a conversational understanding of the domain and other algorithms.

Features:

• MindMeld offers versatile command-line utilities to meet product requirements.
• MindMeld provides advanced NLP, QA, and DM.
• This platform guarantees that your application retains control and ownership.

Pros:

• Only AI platform that supports custom knowledge base creation.
• It helps company users easily discover videos using intuitive voice commands.

Cons:

• The accuracy rate is low with not even competing 95%.
• The verbal variation of language is not very limited and not customized.

Pricing:

• Open platform, free to use.

Link: https://www.mindmeld.com/

5. Amazon Comprehend

Amazon Comprehend has the feature of AI on NLP offers natural language processing, PII detection and redaction, Custom Classification and Entity detection, and topic modeling, allowing a wide range of applications.

Features:

• AWS offers syntax analysis by providing information about structures.
• It requires document classification and multi-language support.
• It can have custom applications.

Pros:

• It has an integration with AWS Services with custom entities and sentiment models.
• It has a topic model with multilingual support.

Cons:

• It is a slow support AI tool with less work.
• Bugs on their interface are a common problem.

Pricing:

• $3 per hour training

Link: https://aws.amazon.com/comprehend/

6. OpenAI

OpenAI is advanced AI tool on NLP with machine learning, NLP, robotics, and deep learning programs. It is responsible for developing generative models with solutions.

Features:

• It has streamlined processes with repetitive tasks and business operations.
• It helps in the process of decision-making by text or data generation.
• It secures advanced analytical skills.

Pros:

• It has large-scale language model exploration with API access.
• With prompt engineering, there is text inference and analysis.
• It includes contextual understanding and versatility in text completion tasks.

Cons:

• There are data privacy and security concerns.
• Certain unconventional encoding methods go undetected.
• The browser plugin is needed for the anti-phishing component.

Pricing:

• $20 per month

Link: https://openai.com

7. Microsoft Azure

It is a leading AI on NLP with cloud storage features processing diverse applications within. It also acts as a text analyst with sentiment analysis and speech recognition. NLP is offered with generating text and understanding languages.

Features:

• Azure supports the development of various applications including Azure AQL.
• It sets on the services for building and managing IoT applications.
• It has scalable solutions with optimization of costs.

Pros:

• It is a cost-effective and good cloud provider.
• It has high availability and data security.

Cons:

• It requires extensive management and platform expertise.
• It has some performance and storage limitations.

Pricing:

• $200 for 12 months

Link: https://azure.microsoft.com/

8. Google Cloud

Google Cloud has the same infrastructure as Google with its developed applications and offers a platform for custom services for cloud computing. It helps in storage with Google Kubernetes Engines. It also has advanced features of AI for NLP.

Features:

• End-to-end machine learning with cloud identity and access management.
• It has pre-trained models for extraction and content classification.
• It has question-and-answer technology language and incorporates training.

Pros:

• It has automated scalability.
• It has quick collaborations with many users.
• It has innovative tools for warehousing with real-time data such as BigQuery.

Cons:

• Google Cloud has limited application engines to Java and Python.
• It has only 3 data centers with less extensive documentation.
• It has expensive traditional hosting.

Pricing:

• $300 plus 20 free products

Link: https://cloud.google.com/free/

9. IBM Watson

One of the common AI tools for NLP is IBM Watson the service developed by IBM for NLP for comprehension of texts in various languages. It is accurate an highly focused on transfer learning and deep learning techniques.

Features:

• It uses machine learning and Watson Assistant as a chat box.
• It is a blockchain technology with an emphasis on protection standards.
• It helps in understanding concepts, extracting metadata, and other concepts.

Pros:

• It is used to process unstructured data contrary to human limitations.
• It also helps in decision-making and analysis systems for available data.
• It has the advantage of sustainability and competitive quantities of data.

Cons:

• It requires a lot of maintenance and continued human checks.
• It requires a lot of steps for structuring data, it is not direct.
• It has limited resources and is seen as a disruptive technology.

Pricing:

• Pay-as-you-go account, 50 free products.
• Payment for only used products.

Link: https://www.ibm.com/inw.ibm.com/in

10. Gensim

Gensim is used by data scientists as an open source with a variety of algorithms and random projections. It is known for data streaming with process large corpora. It relies on Python implementations, and considered as one of the top AI tool for NLP.

Features:

• Key features such as latent allocation, latent semantic analysis, and random projections are included.
• It is used for text processing and customer support.
• The basic feature is related to topic modeling with filtration.

Pros:

• It works on scalability and supports distributed computing.
• It allows for identification between structured and unstructured documents.

Cons:

• It does not support preprocessing capabilities.
• It has limited deep-learning models.

Pricing:

• Free

Link: https://radimrehurek.com/gensim/

11. PyTorch

PyTorch is an optimizer with dynamic features assuming static behavior, and recompiling data sizes. It has compatibility with Python and helps in the Bfloat inference path acting as AI on NLP.

Features:

• It helps in the transition between graph models.
• It supports the development of computers with rich libraries.
• Major clouds of PyTorch have frictionless development.

Pros:

• It has TorchScript which enables models to be in real-time development.
• PyTorch has libraries of NLP and mobile deployment.

Cons:

• It cannot monitor multiple domains.
• It has a smaller community with low-level API.

Pricing:

• $20 per month

Link: https://pytorch.org

12. FireEye Helix

The last AI tool on NLP is FireEye Helix offers a pipeline and is software with features of a tokenizer and summarizer. It has a memory with certain requirements such as name entity tagging and uses cached copy to run fast.

Features:

• It acts as a part of a speech tagger with no dependencies.
• It helps in running complex tools with a single JVM.

Pros:

• It detects any advanced threats with easy integration.
• Helix is easy to analyze and features datasheet detection.
• It has automated volumes for alerts.

Cons:

• It has limited API integrations.
• Usually, burdened with multi-vector attacks.
• Difficulty in integration with SOAR and multiple vendors.

Pricing:

• $30 per endpoint

Link: https://docs.trellix.com

How is NLP used in AI Tools?

AI tools play a key role in using various techniques with deep learning, machine learning, and statistical models. The tools are highly advanced and well worse with the training on large datasheets with certain patterns. Thus, they help in tasks such as translation, analysis, text summarization, and sentiment analysis.

Which is the Best AI Tool for Natural Language Processing?

SpaCy is the best AI Cybersecurity tool as it provides accuracy and reliability with an open library designed for processing data analysis and entity recognition. It is also known for its speech tagging and pre-trained models.

Conclusion

AI on NLP has undergone evolution and development as they become an integral part of building accuracy in multilingual models. They combine languages and help in image, text, and video processing. They are revolutionary models or tools helpful for human language in many ways such as in the decision-making process, automation and hence shaping the future as well.

These AI Tools for NLP are continuously being refined for future endeavors and with the expansion of capabilities, it becomes more user friendly.

AI Tools for Natural Language Processing – FAQs

What is the accuracy of NLP AI tools in understanding any processing language?

The accuracy of the tool depends on the said feature and control or the functioning which is given to the tool. It also includes the quality of training and data based on transformer architectures.

How does NLP use any sentiment analysis?

NLP AI tools can understand the emotional rate expressed and hence identify positive or neutral tones based on the customer’s given functions and operations.

What are real-world applications for AI tools acting in NLP?

Applications shall be translating texts into various languages, text generation, text summarizations, performing analysis functions, and data extraction with chat boxes and virtual assistants.

What process is used by NLP to understand multiple Languages?

The power of analysis is developed to real-time; They use training, language identifiers, fine tunings, Parallel Corpora, multilingual functionaries, and models as data and embedding help in translation in multiple languages.