Top 10 Natural Language Processing (NLP) Tools

Top 10 Natural Language Processing (NLP) Tools

Open-source libraries on the other side are flexible and free. They also allow you to customize your NLP tools. These libraries are designed for developers. You will need to have some experience with machine learning in order to create open-source Natural Language Processing tools. You can count on lots of support because most of these frameworks are community-driven.

What is Natural Language Processing?

Natural Language Processing (NLP) is a discipline of artificial intelligence that leverages linguistics with computer science to make human speech understandable to machines. NLP allows computers to analyze large amounts of data automatically, which can make it possible for you to find meaningful information in a matter of seconds.

How can you get started in NLP? You can find many online tools to help.

Top Natural Language Processing Tools

You can use NLP tools via SaaS (software-as-a-service) or open-source libraries. Natural Language Processing tools can be used to classify support tickets by topic speed up your process and provide powerful insights.

You will need to spend time building your NLP models using open-source libraries. If you don’t have an expert team, you’ll also need to invest money in development.

Also read: Top 15 Machine Learning Tools for Developers

1. MonkeyLearn

MonkeyLearn is a user-friendly NLP-powered platform that helps you to gain valuable insights for text data analysis.

You can start by using one of the pre-trained models to perform text analysis tasks like topic classification, sentiment analysis, keyword extraction, and topic classification. You can create a machine learning model that is tailored to your business for more precise insights.

After you have trained your models to provide accurate insights, you can link your text analysis models with your favorite apps (like Google Sheets or Excel, or Zapier) using our integrations (no coding skills needed) No coding skills required! You can also use MonkeyLearn APIs in any programming language.

2. Aylien

Aylien is a SaaS-based API that uses deep learning and NLP for large amounts of text-based data such as academic publications, real-time content from news outlets, and social media data. It can be used for NLP tasks such as text summarization, article extraction, entity extraction, and sentiment analysis are just a few of the many options.

3. IBM Watson

IBM Watson includes a range of AI services that are stored in the IBM Cloud. Natural Language Understanding is one of the key features. It allows you to identify keywords, categories, and emotions and extract them.

It is versatile in that it can be tailored for different industries such as finance and healthcare. There are also a lot of documents to help you get going.

4. Google Cloud

The Google Cloud Natural Language API offers several pre-trained models, including one for entity extraction, content classification, sentiment analysis, and content classification. It also offers AutoML Natural Language which allows you to create custom machine-learning models.

It is part of Google Cloud infrastructure and uses Google question-answering and language-understanding technology.

5. Amazon Comprehend

Amazon Comprehend integrates with Amazon Web Services. It is an NLP service. This API can be used for NLP tasks like sentiment analysis, topic modeling, entity recognition, etc.

There’s an option for healthcare professionals: Amazon Comprehend Medical, which allows advanced analysis of medical data via Machine Learning.

6. NLTK

Natural Language Toolkit (NLTK) with Python, is one of the most important NLP tools for building NLP models. NLTK is a community that focuses on education and research in NLP. It also offers a variety of tutorials and sample datasets. There are also resources that include a Language Processing and Python handbook.

It can be difficult to learn in this library but it is a great place to start NLP. NLTK has a modular structure that allows for many components to be used in NLP tasks such as tokenization, tagging stemming, parsing, and classification.

Also read: What is a Programming Library: A Complete Guide?

7. Stanford Core NLP

Stanford Core NLP is a popular library that was developed and maintained by Stanford University’s NLP community. It is written in Java, so you will need to install JDK. However, it supports APIs in many programming languages.

Core NLP allows you to perform many NLP tasks such as tokenization, part-of-speech tag and named entity recognition. It is a great choice for complex tasks because of its scalability, optimization for speed, and other advantages.

8. TextBlob

TextBlob is a Python library that works as an extension of NLTK that allows you to perform similar NLP tasks with a more intuitive and user-friendly interface. It is easier to learn than other open-source libraries. This makes it a great choice for beginners who are looking to tackle NLP tasks such as text classification, sentiment analysis, and part-of-speech tagging.

9. SpaCy

SpaCy is one of our newest open-source Natural Language Processing with Python library on our list. It is lightning-fast, simple to use, and well-documented. It is designed to handle large amounts of data. Not to mention, a number of pre-trained NLP models that will make your job easier. Unlike CoreNLP or NLTK, which displays a variety of algorithms for each task. SpaCy has a short menu and offers the best option for every task.

This library is great if you need to prepare the text for deep learning. It also excels at extracting tasks. It is currently only available in English.

10. GenSim

Gensim is a highly specialized Python library that mainly deals with topic modeling tasks through algorithms such as Latent Dirichlet Allocation. It is also capable of recognizing text similarities, indexing texts, and navigating between documents.

This library is fast and scalable. It can handle large amounts of data. These tutorials will help you get started.

Conclusion — Top NLP Tools

Natural Language Processing tools allow companies to gain insights from unstructured text data such as emails, reviews online, social media posts, and many more.

Many online tools make NLP available to businesses, including open-source and SaaS. Open-source libraries allow developers to customize the library in their own way and are free and flexible. They are not cost-effective, and you will need to invest time in building and training open-source tools before you reap the benefits.

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