What are the NLP tools that are most preferred by programmers?

Natural Language Processing or NLP is a subfield of Artificial Intelligence. This technology helps computers and AI understand, interpret, and manipulate human language and the manner we communicate. Though this discipline is still in its nascent stage, there are many incredible ways it is leveraged today viz., chatbot, autocorrect, sentiment analysis, and more. To build and develop NLP applications, programmers use open-source programming languages Python and Java. They also use Node, which is a framework that allows NLP tools to run in JavaScript. Some of the popular tools are used for designing NLP applications are as follows.

NLTK: It stands for Natural Language ToolKit and is an essential library supporting tasks such as classification, stemming, tagging, parsing, semantic reasoning, and tokenization in Python. It's the primary tool for natural language processing and machine learning. It represents all data in the form of strings, which is fine for simple constructs but makes it hard to use some advanced functionality. Today it serves as an educational foundation for Python developers who are new to machine learning.

TextBlob: It is helpful for developers who are starting out with NLP in Python and want to make the most of their first encounter with NLTK. It basically provides beginners with an easy interface to help them learn most basic NLP tasks like sentiment analysis, noun phrase extraction, text classification, part-of-speech tagging, and more. . TextBlob also includes functionality from the Pattern library.  It can be used for rapid prototyping of various NLP models and can easily grow into full-scale projects.

gensim: It is a highly specialized Python library that largely deals with topic modeling tasks using algorithms like Latent Dirichlet Allocation (LDA). It is also excellent at statistical semantics and recognizing text similarities, indexing texts, and navigating different documents. genism has also been designed to extend with other vector space algorithms. Further, it is licensed under the OSI approved GNU LGPLv2.1 license. Also, it is free for both personal and commercial use.

spaCy: It is a relatively young library was designed for production usage. It is more accessible than other Python NLP libraries like NLTK. It offers the fastest syntactic parser available on the market today. As the toolkit is written in Cython, it's also really speedy and efficient. Due to C-like blazing fast performance, spaCy provides a compelling approach to NLP, superior to the rest of the competition. Additionally, it helps in integrating the other data science tools and frameworks.

PyTorch-NLP: It is an excellent python library for quick prototyping. It's updated with the latest research, researchers and apex companies have released many tools to perform total sorts of amazing processing, like image conversations. Though PyTorch is targeted at researchers, it is also be used for workload production and prototypes with the most sophisticated algorithms. This library is used primarily for neural network layers, datasets, and text processing modules.

CoreNLP: It was developed at Stanford University, and it's written in Java. Still, it's equipped with wrappers for many different languages, including Python. Like NLTK, Stanford CoreNLP provides many different natural language processing software. One can use this tool for information scraping from open sources, sentiment analysis, conversational interfaces, and text processing, and generation. It also integrates many of Stanford's NLP tools, like the part-of-speech (POS) tagger, the named entity recognizer (NER), the parser, the coreference resolution system, and bootstrapped pattern learning.

AllenNLP: It is an Apache 2.0 NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks. Built on spaCy, it is simple to use and enables dynamic computation graphs, and provides a flexible data API that handles intelligent batching and padding. AllenNLP also offers high-level abstractions for common operations in working with text, and a modular and extensible experiment framework.

OpenNLP: It is hosted by the Apache Foundation, making it easy to integrate it into other Apache projects, like Apache Flink, Apache NiFi, and Apache Spark. It is a general NLP tool that covers all the common processing components of NLP, and it can be used from the command line or within an application as a library. It also has wide support for multiple languages. OpenNLP also includes maximum entropy and perceptron based machine learning.

Nlp.js: This tool is great for unstructured data applications like translation and chatbots. It identifies 34 different languages and includes a natural language processing classifier and a natural language generation manager. This tool is completely open-source and relies on the contributions of programmers around the world. It is built on top of several other NLP libraries, including Franc and Brain.js.

CogCompNLP: This is developed by the University of Illinois, also has a Python library with similar functionality. It can be used to process text, either locally or on remote systems, which can remove a tremendous burden from your local device. It provides processing functions such as tokenization, part-of-speech tagging, chunking, named-entity tagging, lemmatization, dependency and constituency parsing, and semantic role labeling