📅September 01, 2022
⏱️8 min read

Tools used for Natural Language Processing | NLP

Explore the top tools used in Natural Language Processing (NLP) to effectively analyze and understand human language. Uncover how these cutting-edge technologies are revolutionizing the way we communicate and interact with AI-powered applications.

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Tools used for Natural Language Processing | NLP

Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyse large amounts of natural language data. In simpler words, NLP is concerned with giving computers the ability to understand the text and spoken words like how we humans do. In this blog, we will visit a few tools that are used for Natural Language Processing.

Tools used for Natural Language Processing | NLP



Natural Language Processing enables computers to process human language and understand its meaning you would have interacted with NLP in various forms. Text predictions and suggestions, text speech and speech to text, translators, voice command operated devices. Yes, Alexa, Siri, and your google assistant all utilize NLP to understand and comprehend your commands. Natural language processing consists of several techniques to interpret human languages, such as machine learning and statistical methods to rules-based and algorithmic approaches. Read more about the working of NLP here.

NLTK

The Natural Language Toolkit (NLTK) is a platform used for building Python programs that work with human language data for applying in statistical natural language processing (NLP). It contains text processing libraries for tokenization, parsing, classification, stemming, tagging, and semantic reasoning. In simpler words, NLTK is a toolkit that is used to derive keywords/terms/text from human language aka English. This helps us get insights from surveys, ratings, customer reviews, and any textual input from people. It is the most popular python library and is commonly used for simple text analysis.


SpaCy

SpaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. Compared to NLTK, which is slower and used for simpler analysis, spaCy is faster and can carry complex and advanced analyses. It provides a smoother and efficient analytical experience to the users. SpaCy is good at syntactic analysis, which is handy for aspect-based sentiment analysis and conversational user interface optimization. It can be used for deep learning and sentiment analysis, it excels at large-scale information extraction tasks.


Stanford Core NLP

Stanford Core NLP is a popular library built and maintained by the NLP community at Stanford University it is a multi-purpose tool for text analysis. It provides various NLP services; it has high scalability thereby enabling it to process large data and carry out complex analysis its speed is also an advantage. It is written in Java although it does have API in many programming languages. It can be used for conversational interfaces, sentiment analysis getting insights from data, and so on.


TextBlob

TextBlob is a Python library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. It works as an extension of NLTK as it enables us to use several features of NLTK in a simplified and user-friendly manner.


Apache OpenNLP

Apache OpenNLP is a machine learning-based toolkit for the processing of natural language text. OpenNLP supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection, and coreference resolution. Similar to Stanford CoreNLP, OpenNLP also uses Java.

There are several other toolkits, libraries, and tools that are utilised for NLP like AllenNLP, Textacy, PyTorch-NLP, Intel NLP Architect, Google Cloud NLP API, IBM Watson, Amazon Comprehend, and many more. All of these tools enable NLP for different types of analysis and deriving business insights, helping us understand and utilise bulk data to our benefit.

If you are looking to incorporate conversational AI into your business/firm a small and smart first step would be to start with a chatbot and Conferbot can help you with just that. Why Conferbot? They use machine learning algorithms that improve with every conversation and sound more natural and personalized than ever. You will never miss a query from your customers their bot adapts to any industry, specializes in the challenges you face. They are a one-stop solution.

Similar blogs:
https://opensource.com/article/19/3/natural-language-processing-tools
https://monkeylearn.com/blog/natural-language-processing-tools/
https://theappsolutions.com/blog/development/nlp-tools/

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This comprehensive guide on "Tools used for Natural Language Processing | NLP" will teach you practical AI chatbot strategies and automation techniques. Explore the top tools used in Natural Language Processing (NLP) to effectively analyze and understand human language. Uncover how these cutting-edge technologies are revolutionizing the way we communicate and interact with AI-powered applications. You'll discover step-by-step implementation methods, best practices for Chatbots, and real-world examples you can apply immediately to improve your customer engagement and business processes.

Most strategies covered in "Tools used for Natural Language Processing | NLP" can be implemented within 15-30 minutes using Conferbot's no-code platform. The guide provides quick-start templates and ready-to-use chatbot flows for Chatbots. Simple chatbots can be deployed in under 5 minutes, while more complex implementations may take 1-2 hours depending on your specific requirements and integrations.

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