10 Top NLP Tools in 2022

Machine learning has a lot of sub-fields within it that serve several different purposes. If you are here reading this blog, you must have some knowledge about NLP. Even if you don’t, we have got everything covered in this blog.

 

What is NLP?

 

Natural Language Processing is a subfield of Machine learning that helps the machines understand and manipulate the natural language spoken by humans with the help of software, aka NLP tools. 

 

Before we get into the different NLP tools, we need to understand the purposes for which we would use these tools.

 

Many businesses today rely on social media and other sources online like emails, chats, and websites for their businesses. It is one of the best ways to figure out customer insights and predict customer behaviour. 

 

But most of them are scattered and unstructured and can get overwhelming for the employees to sort manually. This is where NLP comes to the aid. 

 

By allowing computers to automatically evaluate vast volumes of data, Natural Language Processing can help you find useful insights in unstructured text and tackle several text analysis challenges, such as sentiment analysis, topic categorization, and more.

 

But, for beginners, starting with NLP can also be a little difficult. NLP tools can solve that problem as well. Many NLP tools in the market can be accessed as SaaS tools or open-source libraries. 

 

• SaaS products are ready-to-use, efficient cloud-based solutions that require little or no coding to implement. 

 

SaaS platforms frequently include pre-trained NLP models that may be utilized without writing code, as well as APIs intended at individuals who prefer a more flexible, low-code option, such as professional developers or those beginning to code.

 

• On the other hand, open-source libraries are free, versatile, and allow you to fully customize your NLP tools. They're geared for developers, so they're a little more difficult to comprehend, and you'll need machine learning knowledge to construct open-source NLP tools. 

 

If you don't already have an in-house team of specialists, you'll need time to construct infrastructures from scratch and money to invest in developers to design your NLP models using open-source libraries.

 

Best NLP tools

 

Open-source tools:

 

1. NLTK:

 

Natural Language Toolkit (NLTK) is a Python NLP-powered open-source program for beginners. As a result, the NLTK library has become a standard NLP tool for research and education. 

 

NLTK gives users a basic collection of tools to do text-related tasks. It includes methods like text categorization, entity extraction, tokenization, parsing, stemming, semantic reasoning, and more, making it a useful place to start for novices in Natural Language Processing.

 

For rudimentary text analysis, the Natural Language Toolkit is handy. Try something different if you need to work with a large volume of data. Why? Because Natural Language Toolkit demands a lot of resources in this scenario.

 

2. SpaCy:

 

It's a Python and Cython library that may be used together. It's a follow-up to NLTK that includes pre-trained statistical models and word vectors. It now supports tokenization for more than 49 languages.

 

In terms of working with tokenization, this library can be considered one of the finest. It enables you to divide the text into semantic chunks such as words, articles, and punctuation. 

 

SpaCy is well-equipped with all of the functionality required in real-world projects. It also has the fastest and most accurate syntactic analysis of any NLP package available.

 

3. Stanford NLP Library:

 

The App Solution says the Stanford NLP library may be described as a multi-purpose text analysis tool. Stanford CoreNLP, like NLTK, offers a variety of natural language processing applications. 

 

Custom modules, on the other hand, can be used if you require more. Scalability is the key benefit of Stanford NLP technologies. Stanford Core NLP, unlike NLTK, is ideal for handling vast volumes of data and executing sophisticated computations.

 

Stanford CoreNLP's great scalability makes it an ideal candidate for:

 

• collecting data from public sources (social media, user-generated reviews)

• examination of public opinion (social media, customer support)

• Conversational interactions (chatbots)

• text creation and processing (customer support, e-commerce)

 

4. GPT-3:

 

Open AI just published GPT-3, a new tool. It's fashionable while also being strong. It is an autocompleting application since it is mostly used for text prediction. GPT-3 will produce something comparable but uniquely based on multiple instances of the desired text.

 

The GPT project is always being worked on by Open AI. It's nice in the third version. The sheer volume of data on which it was pre-trained is a significant benefit (175 billion parameters). You can get outcomes that are closer to actual human language if you use it.

 

5. Apache OpenNLP:

 

When you require a tool for long-term usage, accessibility is critical, which is difficult to come by in the world of Natural Language Processing open-source technologies. Because, although having the necessary functionality, it may be too difficult to use.

 

For individuals who desire pragmatism and accessibility, Apache OpenNLP is an open-source library. It leverages Java NLP libraries with Python decorators, just like Stanford CoreNLP.

 

While NLTK and Stanford CoreNLP are cutting-edge libraries with a plethora of features, OpenNLP is a straightforward yet helpful tool. Furthermore, you may customize OpenNLP to your needs and remove features that aren't required. For Named Entity Recognition, Sentence detection, tokenization, and POS tagging, it is one of the finest options.

 

Also Read | Top 10 Open Source Low Code Platforms

 

SaaS platforms:

 

6. Google Cloud:

 

Several pre-trained models for sentiment analysis, content categorization, and entity extraction are available through the Google Cloud Natural Language API. It also has AutoML Natural Language, which allows you to create your own machine learning models.

 

As part of the Google Cloud architecture, it leverages Google question-answering and language comprehension technologies.

 

7. Unbable:

 

According to saasworthy, Unbabel is a multilingual customer service system that delivers next-level support to its consumers. The software's intrinsic quality translation makes users' support teams bilingual, lowering costs and response times while simultaneously improving customer happiness. 

 

The platform eliminates language as a job requirement, allowing customers to form their best teams based on product expertise and support abilities. 

 

Support operations become more nimble and effective thanks to the completely scalable support translation, which allows users to increase team productivity, optimise shifts, and reduce logs. Users may also deliver cost-effective assistance from key locations and maximise coverage for long-tail, costly, and difficult-to-hire languages.

 

Users may take their deflection strategy to the next level with the multilingual chatbot and FAQs, lowering expenses. The AI-powered human-refined combines the brand's style guides and bespoke glossaries.

 

8. Text Blob:

 

It appears to be the fastest machine learning tool on the market. Text Blob is another NLTK-based natural language processing tool that is easily available. Additional features that allow for additional textual data might improve this.

 

Through speech recognition, Text Blob sentiment analysis may be utilised for customer contact. Furthermore, you may create a model employing a Big Business trader's linguistic skills.

 

Another useful Text Blob function is a machine translation. Content standardisation has become commonplace and beneficial. If your website/application could be automatically localised for this reason, it would be fantastic. The language text corpora from Text Blob can be utilised to improve machine translation.

 

9. Amazon Comprehend:

 

Amazon Comprehend is a natural language processing (NLP) service that is embedded into the Amazon Web Services architecture. This API may be used for sentiment analysis, topic modelling, entity recognition, and other NLP applications.

 

It is used to extract useful information from text in documents, customer service issues, product evaluations, emails, social media feeds, and other sources. It may also make document processing workflows easier by extracting text, key words, subjects, sentiment, and other information from documents like insurance claims. (here)

 

10. IBM Watson:

 

IBM Watson is a collection of artificial intelligence (AI) services hosted on the IBM Cloud. Natural Language Understanding is one of its primary capabilities, allowing you to recognise and extract keywords, categories, emotions, entities, and more.

 

It's adaptable, since it can be modified to a variety of sectors ranging from healthcare to banking, and it comes with a library of papers to get you started.

 

Conclusion

 

Natural Language Processing (NLP) solutions are assisting businesses in extracting information from unstructured text data such as emails, online reviews, social media postings, and more. Many internet technologies, such as open-source and SaaS, make NLP accessible to your organisation. 

 

Open-source libraries are costless, versatile, and allow developers to completely change them. They are, however, not cost-effective, and you will have to invest time in developing and teaching open-source technologies before reaping the rewards.

 

Individuals or businesses can utilise the five open source tools and five SaaS technologies specified in this article to develop an NLP model. It can be used according to one's personal tastes.