What Are the Most Profitable NLP Tools to Grow Your Startups?
Natural Language Processing (NLP) is a branch of AI that deals with human-computer interaction. It is often used to comprehend text and spoken language in the same way as humans do.
NLP can undoubtedly aid in the preservation and extraction of efficient insights from any data set. It also helps in maintaining and classifying the original data set and documents.
Natural Language Processing (NLP) can be an excellent and promising tool if you plan to build a chatbot, voice assistant, or any other type of text or speech-based application. There are various sorts of NLP tools that have been developed in the past few years, and most of them are open-sourced.
Integrating natural language processing tools and technologies with deep learning models has significantly increased the generation of models such as Chat GPT and GPT-3. All of these models are particularly good at creating artificial intelligence-based text that simulates human writing.
For instance, GPT-3 grants access to capabilities available in Microsoft's brand-new "no-code" Power Apps platform. Here using such a platform enables you to attain a query of any natural language description.
Here are some valuable and accessible open-source tools for natural language processing:
A. Python and NLP-based tools-
1. Natural Language Toolkit (NLTK):-
NLTK is a widely used tool for natural language processing in artificial intelligence. We can use a lot of features and functionalities based on categorization, vectorization, and tokenization.
There are often multiple implementations for each feature, so you can choose the precise algorithm or methodology you want to use. NLTK also supports many languages. However, all data is represented as strings, which is fine for simple structures but can make it challenging to use some of the more advanced functionality.
2. spaCy:-
spaCy is a powerful NLP toolkit that is fast and easy to use. Here the development and execution of applications are extremely simple to comprehend. Additionally, everything is represented as an object, which makes it easier to work with other frameworks and tools.
This tool has a simple interface with limited choices, excellent documentation, and multiple neural models for various language processing and analysis tasks. The tool becomes an effective solution for new applications to increase their performance.
3. TextBlob:-
If you're starting with natural language processing in artificial intelligence, TextBlob might be an excellent tool to use while learning. It is easy to understand because of its simplified nature, and you can use it for production projects that don't need to be super fast, and it's used all over the place for smaller projects.
4. Textacy:-
The Textacy library is one of the most user-friendly and powerful NLP tools available. Textacy is the integration of spaCy and core NLP functions, which can be a better option for handling and processing data. You can use Textacy, as an alternative for the spaCy which will be an excellent tool for reducing the code length for any data type.
5. MonkeyLearn:-
MonkeyLearn is a quick and adaptable NLP-based tool that can be used to collect and recognize information from a set of data. This platform can be utilized for content analysis, categorization, data extraction, and text analytics. It can also be connected to any Google Sheets or Spreadsheets to perform text analysis from the huge dataset.
6. PyTorch-NLP:-
Although the PyTorch-NLP tool has only been available for a little over a year, it has already amassed a sizable user base. PyTorch is an excellent tool for rapidly prototyping product ideas.
It's also kept up-to-date with the latest research findings and advances, with many different tools available to do things like image processing and other amazing transformations. Researchers have been using PyTorch as a prototyping tool with the most cutting-edge algorithms in recent days.
B. Node-based tools:-
7. ReText:-
ReText is a powerful syntax that allows different tools and plugins to work together in order to achieve a particular goal. It's used by the unified tool, which is an interface that allows for different software to communicate and work together efficiently.
This is an exciting idea, and the community surrounding it is only growing larger. ReText employs plugins to get the outcomes you might be looking for using NLP tools but doesn't provide many of its underlying strategies.
8. WinkNLP:-
WinkNLP is a Node-based natural language processing tool that can be used quickly and effectively to deploy and maintain NLP-based applications. Without relying on any other code, you may fully automate winkNLP to balance performance and accuracy.
9. Compromise:-
Compromise is not the most advanced tool in the category of NLP tools and technologies, but it can provide accessibility to a range of capabilities and functions. Compromise can effectively help in delivering excellent features which can be easily considered for various sorts of NLP tasks.
Overall, the name is accurate, and Compromise is an excellent tool for those who want something simple and effective.
10. NLP.js:-
NLP.js is an excellent tool for anyone looking to get started in natural language processing in artificial intelligence. Here it provides a very user-friendly interface for creating various types of tasks related to NLP, like classification, named entity recognition, etc.
NLP.js also supports various languages, making it an excellent choice for international applications.
C. Java-based tools:-
11. OpenNLP:-
OpenNLP is an excellent approach for natural language processing tools. This tool is basically administered by the Apache foundation and can be easily used for various sorts of Apache projects.
OpenNLP has a wide range of features and supports multiple languages. This could be used as an open-source library from the command line or inside of an application. If you're using Java, OpenNLP is ready for production workloads.
12. Stanford CoreNLP:-
Stanford CoreNLP is a prominent tool that can initiate a wide range of NLP capabilities that involves deep-learning, statistical, and rule-based NLP.
It was created by one of the world's leading research institutions, but it may not be the best choice for production workloads.
This tool is dual-licensed, which means there may be additional costs involved if you use it in a commercial setting. Overall, Stanford CoreNLP is an excellent tool for research and experimentation, but you should keep the potential costs in mind before using it in a production system.
13. CogCompNLP:-
The CogComNLP is a potent tool that can be effectively used in text processing remotely on a server, which tends to decrease the efforts on the local storage and architectures. It offers a number of capabilities that result in labeling, NER (Name Entity Recognition), chunking, and processing operations.
14. NLP4J:-
NLP4J is an acronym for Natural Language Processing for JVM languages. This provides NLP tools and frameworks for easier and faster API deployment in computational architecture. It essentially operates under the Apache 2 license and is compatible with most of the NLP disciplines.
15. ReVerb:-
ReVerb is an excellent NLP program that can easily recognize and extract binary connections from English sentences. It can be advantageous because it has a faster processing speed and has been proven to be a practical tool for beginners to use and learn NLP frameworks.
Summing up:-
We hope you enjoyed the blog post about open-source natural language processing tools for developers. The presentation of the tools we covered should help you search for a natural language processing solution that can help you with your business.
If you have questions about any of the tools or the content you have read, don't hesitate to visit our website and do check out our Advanced Data Science and AI Program with Domain Specialization.
