Top 10 Python Libraries For NLP With Applications

Top 10 Python Libraries For NLP With Applications

Introduction 

With the increase in popularity of AI and deep learning techniques, business owners are looking to hire NLP (natural language processing) software for their business. The best Language Processing software is built with Python to automate data collection and analysis. An NLP tool can help enterprises understand customers better by using automated sentiment analysis and text parsing techniques.

 

Python is a surprisingly elegant yet powerful programming language. Whether you’re working at a major corporation, running a start-up, or looking to make your mark in the tech world, Python language helps create the perfect solution for your next project. Software developers can automate programming tasks and increase productivity with a robust and extensive library of software engineering tools that are open source.

 

Top 10 Python Libraries 

 

Python libraries are packages of pre-written code that eliminate the need for writing and maintaining codes from scratch. Developers can use today’s libraries to create machine learning and data science solutions, data visualizations, and image and data manipulation applications. The list of libraries is growing and is expected to reach over 137,000 by 2022. Some libraries are available based on TensorFlow for deep learning, Pandas for data analysis, Flask and Django for web application development, and NumPy and SciPy for scientific computing. Since Python is an interpreted language, it can be efficiently run on Windows without any need for compilation.

 

Choosing the best one that suits your needs is not an easy task, and selecting the wrong library may cost you more than you expect. Here we list the top 10 Python Libraries 2022 to help you develop efficient applications, improve your productivity, and save time.

 

#10 Polyglot

Polyglot is an open-source python library for natural language processing based on NumPy and is designed to be faster than spaCy. It will be a good choice if you need to process large collections of texts in multiple languages. It provides you with APIs to manipulate texts in a variety of languages and extract structured data from documents. The library uses various state-of-the-art techniques in the field of natural language processing and has several applications like named entity recognition, dependency parsing, or relationship extraction.

 

#9 Scikit 

The scikit-learn Python package is an open-source software library for machine learning built on top of SciPy and NumPy. It features various classification, regression and clustering algorithms, including support vector machines, random forests, and gradient boosted trees.

Scikit-learn

It has a diverse range of algorithms, comes with extensive documentation, and enables beginners to easily design classification processes by implementing various methods like text classification.

 

#8 Textblob

textblob-logo

If you deal with natural language, TextBlob is probably the most accessible library for you. It provides basic text processing capabilities such as noun phrase extraction, sentiment analysis, classification, and translation. It is based on NLTK, and it is easily extensible with your custom models. It works with tokenization, word inflection, classification, part-of-speech tagging, and more — ideal for parsing data or building a natural language interface.

 

#7 Pineapple

PyNLPl is a Python library that allows you to compile and manipulate frequency lists, develop language models, generate engrams, and much more. It has a powerful and extensive library for work with Folio XML technology. This interactive learning tool gives you a place to start and instant feedback as you write code.

 

#6 Core NLP

CoreNLP is a robust Java-based application that provides text analysis and processing of natural language. One of the most popular NLP tools developers use for data analysis, machine learning, and Natural Language Processing applications. Stanford’s CoreNLP does not provide any visualizations or interactive user interface, but it offers a well-documented API with which developers can integrate it with their applications. 

 

#5 Pattern 

The pattern is highly developed in terms of NLP. It is often used for sentiment analysis and POS tagging. Pattern brings together a ton of libraries and other tools that help develop NLP solutions, along with its platform for data mining. Pattern’s popularity is largely because it is user-friendly, open-source, and fast to develop. You don’t have to be a technology guru to get started with neural networks or deep learning.

 

#4 flare

FLARE is a new multimedia library for the Python language that provides a rich set of graphics, audio, and video features – from easy high-level declarative commands to low-level access to the underlying operating system. FLARE is also the wrapper package for the PyTorch Reinforcement Learning Framework.

 

# 3 Gensim

gensim-logo

Gensim is a free, open-source library for Natural Language Processing (NLP). The tool is handy for topic modeling and document retrieval. It is based on the idea of finding semantically similar words by using their context or semantic similarity. Gensim has a simple and efficient algorithm for vector representation of words. It’s an excellent product for those who need to access high-level processing speeds and handle massive chunks of data.

 

#2 Spacey

The Spacy NLP library has pre-trained statistical models, tokenization in various languages, and a blazing fast speed. Suppose you need a service that excels at massive scale extraction tasks for data management. In that case, this is the library for you — with the highest accuracy in a wonderful ever-growing ecosystem.

 

#1 NLTK

NLTK provides a developer with access to 50 linguistic and corporate resources, an easy-to-use interface, and a suite of text processing tools that help when you want to classify or pass information. It is also a widely-used solution in the Python programming language, and there is much documentation online that can help even the first-time user.

 

Conclusion 

top-10-python-libraries

There are many ways to do Natural Language Processing with Python. If you’re interested in building a chatbot, implementing sentiment analysis on your blog, or just saving yourself a lot of time formatting documents, then you have these top 10 Python libraries for NLP with real-world applications.

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