Best 10 Natural Language Processing tools for Professionals

Introduction to Natural Language Processing

Natural Language Processing is a subset of Artificial Intelligence that deals with human language. NLP encodes the natural human language so the machine can interpret and understand it. It help us to apply statistical models and analysis on human language to gain inference and insight into human behavior, communication, and speech patterns.

Businesses and enterprises use it to understand customer behavior and market trends. They create applications to improve service delivery, such as Chatbots, Voice assistance, etc. These days it is also applied to filter out applications and documents or gain insights from textual documents. On the other hand, researchers use NLP to build complex statistical models to understand human behavior or replicate it.

With the rise in research and development of Natural Language Applications, it is essential to know about available Natural Language Processing Tools to choose the best combination of tools for the project. NLP Tools provide the functionality to perform it, such as tokenizing words, tagging parts of speech, etc.

What are the features of Natural Language Processing?

Some commonly used features are described below:

  • Tokenization: It is the procedure of separating pieces of text into single units the machine can understand.
  1. Part of speech tagging: Tagging words based on their grammatical significance, such as nouns, verbs, etc.
  2. Bag of words: A subset of words from the dataset vocabulary, often used to vectorize data points. E.g., a movie can be vectorized based on words present and absent in its textual description.
  3. Named entity recognition: Locating and classifying named entities in textual data, such as company name, country, city, etc.
  4. Topic modeling: Unsupervised learning method to cluster documents under topics, done by studying and comparing the words used in the documents.
  5. Classification: Classification of objects based on the words used in their descriptions, tags, etc.
  6. Keyword analysis: Analyzing specific keywords used in textual data.
  7. Sentiment analysis: Recognizing and classifying sentiments expressed in sentences or documents.

Natural Language Processing Tools

NLP tools can be open-source libraries used for research and development of applications. On the other hand, these tools can also come in fully managed paid applications or software as a service, where the developers have operationalized their pre-trained models. Following are some commonly used tools :


Natural Language Toolkit, or NLTK, is an open-source Python library that contains fully featured tools. It provides a wide variety of features such as tokenization, stemming, tagging, classification, a bag of words, etc., almost everything you need to work with natural language as a developer. NLTK stores the textual data in the form of strings. Thus it can take more work to integrate with other frameworks. It was built to support education and research in natural language processing.


SpaCy is also an open-source library under Python with optimized features and models for it. In NLTK, where one would have to choose tools from a wide variety of tools, SpaCy offers only a selected set of tools that are best among their competitors to save time and confusion for developers. SpaCy also works with text stored in the form of objects, making it easier to integrate with other frameworks.


Word2Vec is an NLP tool used for word embedding. Word embedding is representing a word in the form of a vector. Words are converted to vectors based on their dictionary meaning, and these vectors can be used to train ML models to understand similarities or differences between words.

Amazon Comprehend

Amazon’s Comprehend is software as a service. It gives the user inference from the analysis of textual documents. It simplifies the document processing job of the users by extracting text, key phrases, sentiment, topic, etc., from the documents. It also provides model training based on the classification of documents.


GenSim is an open-source python library used for topic modeling, recognizing text similarities, navigating documents, etc. GenSim is very memory efficient and is a good choice for working with large volumes of data since it does not need the whole text file to be uploaded to work on it.

Core NLP

It is a Java-based open-source library used for parts of speech tagging, tokenization, and named entity recognition, as well as automatically decoding dates, times, and numbers. It is very similar to NLTK and has APIs for languages other than Java. It has the advantage of scalability and is faster processing textual data. CoreNLP offers statistical, deep learning, and rule-based NLP functionality, which is excellent for research purposes.

Google Cloud Natural Language

Google Cloud Natural Language API consists of pre-trained models for text classification, sentiment analysis, etc. It allows you to build your machine-learning models using Auto ML features. This API uses Google’s language understanding technology and thus is a perfect choice when working on projects that require high accuracy.


Generative Pre-trained Transformer is a tool created by OpenAI for text generation. It was trained on a sizeable textual dataset and can generate text similar to natural human language. GPT can be used to autofill documents, generate content for websites or blogs, etc.


CogCompNLP is a tool developed at the University of Pennsylvania. It comes in Python and Java and is stored locally or remotely for textual data processing. It provides functions such as tokenization, part-of-speech tagging, chunking, lemmatization, semantic role labeling, etc. It is capable of working with big data and remotely stored data.


TextBlob is another one of the python open source tools that are built upon NLTK. TextBlob includes a lot of NLTK functionalities without the complexity. Thus, it is a good choice for beginners. It also includes features from python’s Pattern library and can be used for production applications that do not have specific algorithmic requirements.


Natural language processing has many applications throughout the industry, from automatic form filling and analyzing resumes of thousands of applicants to creating 170 million parameter large language models that can generate text from keywords. Various its tools are available, open-source, and applications (software as a service) are developed by organizations. Tools like NLTK are industry classics that provide all the features required to build NLP applications. In contrast, applications like Amazon Comprehend provide pre-trained complex models that can be used to gain values, insights, and connections in text.

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Best 10 Natural Language Processing tools for Professionals
bindu singh

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8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.

Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.

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Ray  Patel

Ray Patel


Introduction to Natural Language Processing

We’re officially a part of a digitally dominated world where our lives revolve around technology and its innovations. Each second the world produces an incomprehensible amount of data, a majority of which is unstructured. And ever since Big Data and Data Science have started gaining traction both in the IT and business domains, it has become crucial to making sense of this vast trove of raw, unstructured data to foster data-driven decisions and innovations. But how exactly are we able to give coherence to the unstructured data?

The answer is simple – through Natural Language Processing (NLP).

Natural Language Processing (NLP)

In simple terms, NLP refers to the ability of computers to understand human speech or text as it is spoken or written. In a more comprehensive way, natural language processing can be defined as a branch of Artificial Intelligence that enables computers to grasp, understand, interpret, and also manipulate the ways in which computers interact with humans and human languages. It draws inspiration both from computational linguistics and computer science to bridge the gap that exists between human language and a computer’s understanding.

Deep Learning: Dive into the World of Machine Learning!

The concept of natural language processing isn’t new – nearly seventy years ago, computer programmers made use of ‘punch cards’ to communicate with the computers. Now, however, we have smart personal assistants like Siri and Alexa with whom we can easily communicate in human terms. For instance, if you ask Siri, “Hey, Siri, play me the song Careless Whisper”, Siri will be quick to respond to you with an “Okay” or “Sure” and play the song for you! How cool is that?

Nope, it is not magic! It is solely possible because of NLP powered by AI, ML, and Deep Learning technologies. Let’s break it down for you – as you speak into your device, it becomes activated. Once activated, it executes a specific action to process your speech and understand it. Then, very cleverly, it responds to you with a well-articulated reply in a human-like voice. And the most impressive thing is that all of this is done in less than five seconds!

#artificial intelligence #big data #data sciences #machine learning #natural language processing #introduction to natural language processing

Paula  Hall

Paula Hall


Structured natural language processing with Pandas and spaCy

Accelerate analysis by bringing structure to unstructured data

Working with natural language data can often be challenging due to its lack of structure. Most data scientists, analysts and product managers are familiar with structured tables, consisting of rows and columns, but less familiar with unstructured documents, consisting of sentences and words. For this reason, knowing how to approach a natural language dataset can be quite challenging. In this post I want to demonstrate how you can use the awesome Python packages, spaCy and Pandas, to structure natural language and extract interesting insights quickly.

Introduction to Spacy

spaCy is a very popular Python package for advanced NLP — I have a beginner friendly introduction to NLP with SpaCy here. spaCy is the perfect toolkit for applied data scientists when working on NLP projects. The api is very intuitive, the package is blazing fast and it is very well documented. It’s probably fair to say that it is the best general purpose package for NLP available. Before diving into structuring NLP data, it is useful to get familiar with the basics of the spaCy library and api.

After installing the package, you can load a model (in this case I am loading the simple Engilsh model, which is optimized for efficiency rather than accuracy) — i.e. the underlying neural network has fewer parameters.

import spacy
nlp = spacy.load("en_core_web_sm")

We instantiate this model as nlp by convention. Throughout this post I’ll work with this dataset of famous motivational quotes. Let’s apply the nlp model to a single quote from the data and store it in a variable.

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Sival Alethea

Sival Alethea


Natural Language Processing (NLP) Tutorial with Python & NLTK

This video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and more. Python, NLTK, & Jupyter Notebook are used to demonstrate the concepts.

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