When Using NLP, Which Language Is Best, and Why?

At the junction of artificial intelligence (AI) and data science is natural language processing (NLP). This area of research is dedicated to the development of software and robots that can understand and interpret human speech. There are multiple languages available for NLP programming, with Python being the most frequently used. In this article, we will explore the various Python modules used for NLP and investigate why Python has become so popular for this purpose. Additionally, we will give a brief overview of the other languages employed in NLP programming.

Using Python for Natural Language Processing Has Several Advantages.

Natural language processing (NLP) is currently receiving a great deal of attention, and for good reason. NLP can provide valuable solutions and insights to customers who are hindered by language barriers. Companies such as Facebook, Google and Amazon are investing millions of dollars in NLP technology to power their virtual assistants, product search platforms, chatbots and other services which are enabled by NLP.

Prior to this time, those with extensive knowledge and experience in the fields of processing algorithms, machine learning, linguistics, mathematics, and other related areas, were the only ones who had access to Natural Language Processing (NLP) projects. Now, developers can save time and energy on text processing by utilising the existing tools and environment for NLP. Python and its associated libraries and tools are particularly well-suited for addressing various NLP challenges.

Some of the many advantages of using Python for NLP applications are as follows:

  • Python is a great option for projects because of its clear syntax and semantics.
  • To create machine learning models, Python developers have strong support for integrating with other languages and technologies.
  • There is an abundance of tools and frameworks for natural language processing (NLP) available in Python, enabling developers to easily perform tasks such as sentiment analysis, part-of-speech (POS) tagging, document categorization, topic modelling, word vectorization, and much more. By leveraging the vast selection of Python-based NLP tools, developers can quickly and efficiently build powerful applications that can accurately process natural language data.

Some of the Best Python NLP Libraries

Resource for Analysing and Modifying Languages (NLTK)

The Natural Language Toolkit (NLTK) is a powerful Python library developed by Edward Loper and Steven Bird for a diversity of natural language processing (NLP) tasks. It is a highly esteemed resource for NLP in Python, providing a reliable foundation for Python developers who are engaged in NLP and machine learning (ML) development.

Despite its robustness and flexibility, the library may prove to be difficult to utilise for Natural Language Processing (NLP) applications. Its performance is slow and does not properly support the accelerated production processes that are required. Additionally, there is a large learning curve associated with it. However, Python developers have access to various resources, such as documentation and tutorials, to help them gain a better understanding of the language.


The TextBlob package is an invaluable tool for Python developers just beginning to explore the world of Natural Language Processing (NLP). TextBlob provides a straightforward and user-friendly interface that simplifies the process of mastering fundamental NLP tasks such as Part-of-Speech tagging, word extraction, sentiment analysis, and more. In short, TextBlob is an essential asset for Python developers wishing to take their NLP skills to the next level.

If you are new to natural language processing in Python, TextBlob is an excellent choice to consider for creating prototypes. Nonetheless, it has the downside of being slow and inadequate to satisfy the requirements of more business-oriented natural language processing projects.


CoreNLP, a natural language processing library created at Stanford University and written in the Java programming language, is a great choice for developers looking to get started with natural language processing in Python. The library is capable of supporting multiple languages, including Python, and has a low response time, making it a reliable option for use in production settings. Furthermore, parts of CoreNLP can be combined with NLTK to increase productivity.


Gensim is an advanced framework that allows users to identify the semantic similarities between texts through the utilisation of topic modelling and vector space modelling techniques. By leveraging incremental algorithms and data streaming, Gensim is capable of managing large-scale text corpora effectively.

When compared to other programs that are limited to in-memory and batch processing, Gensim has an advantage when it comes to processing large collections of text. This library is able to achieve its superior processing speed and memory efficiency due to the utilisation of NumPy. Not only does Gensim possess advanced features, but it also has advanced vector space modelling capabilities.


The utilisation of spaCy is a relatively recent development. Designed for robust applications, spaCy provides access to a larger range of word vectors. Furthermore, it provides the industry’s most rapid parsing speeds due to its utilisation of Cython as its programming language, resulting in a quick and efficient experience.

In recent years, there has been an increasing focus on areas such as Machine Learning, Artificial Intelligence, and Natural Language Processing, resulting in the emergence of spaCy as a critical library despite its limited language support. This provides an optimistic outlook for the eventual incorporation of further languages.


PolyGlot is an often overlooked Python library that deserves recognition for its impressive range of supported languages and powerful analysis capabilities. Thanks to its integration with NumPy, the library is highly efficient and shares many similarities with spaCy. Additionally, Polyglot makes use of pipeline principles to facilitate the execution of complex commands, and it has the capacity to communicate with an extensive array of languages.

With its broad range of language support and comprehensive analytic capabilities, PolyGlot is highly regarded among experts. It is an excellent choice for projects that do not require an exacting level of adherence to SpaCy standards.

Some of PolyGlot’s most intriguing characteristics and statistics are as follows:

  • This website provides transliteration for 69 different languages.
  • Context-aware word embeddings in 137 languages
  • Comparative Morphological Study of 135 Languages
  • A total of 165 languages are supported by the tokenization system.
  • Detection of languages – 196 in total
  • Identifying parts of speech in 16 languages
  • Accurate name recognition in 40 different tongues
  • Analysing how people feel in 136 different tongues


Scikit-learn is an incredibly useful Python package for developers looking to create machine learning models and other processes, due to its wide variety of methods. Furthermore, it has several additional features which can be used to craft unique characteristics that can address categorization problems. Its key selling point is its user-friendly class feature, which makes it easily accessible in Python. Additionally, it offers extensive documentation to help programmers get the most out of the tool.

It should be noted that scikit-learn does not include neural networks as a part of its text processing capabilities. If you need to process text for machine learning, you should look to alternative natural language processing (NLP) tools before coming back to scikit-learn to construct your models.


When it comes to working with natural language, Pattern is one of the most robust and popular libraries available. It simplifies:

  • Point-of-sale (POS) tagging
  • Modelling in a vector space
  • Clustering
  • SVM
  • N-gramme analysis
  • Identifying and analysing feelings
  • WordNet

The Pattern framework provides access to a web crawler, DOM parser, and other helpful APIs.


AllenNLP is a state-of-the-art natural language processing (NLP) technology that has considerable potential for both commercial and academic applications. Built on libraries and PyTorch tools, this deep learning library for NLP provides a user-friendly experience and integrates with spaCy for data preparation.

AllenNLP is an invaluable tool for those who wish to construct models from the ground up, as well as those who wish to manage and evaluate experiments. It facilitates the entire process, from the initial design of the model to the arrangement of experiments involving multiple components, with the goal of accelerating and optimising the process. Furthermore, AllenNLP allows users to conduct the essential customer feedback and purpose analyses necessary for the development of services and products.


Python’s Vocabulary is an invaluable tool for Natural Language Processing (NLP) professionals. It provides users with a comprehensive array of related words, antonyms, pronunciations, and other pertinent information related to any given term. The value of the information is returned in basic JSON objects, similar to the way values are returned in Python lists and dictionaries. There are numerous advantages to using Python’s Vocabulary, including its speed and ease of setup and its user-friendly interface.

The utilisation of Python for natural language processing can be further optimised through the utilisation of the packages discussed in this article. However, similar results can be achieved by utilising alternative languages. In this section, we will discuss the relevant languages and the books available for reference for each of them.

Natural Language Processing Languages Other Than Python


When it comes to NLP, Java is the programming language of choice because of its robustness. It opens the door to a variety of disciplines, like as

  • Scheduling the results of a full-text search
  • Clustering
  • Extraction
  • Tagging

Java can run on any system and processes data rapidly and effortlessly. The best two libraries for natural language processing tasks are shown below.

Python Natural Language Processing Using Apache OpenNLP

This open-source Natural Language Processing (NLP) Java library provides an effective learning-based toolset for analysing text written in a wide range of languages. The library is composed of a number of components which facilitate the construction of NLP pipelines, making the process simpler and more efficient.

  • Analyse names and suggest new ones
  • Tokenizer
  • Organiser for sorted documents
  • Point-of-Sale Tagger
  • Parser
  • Chunker
  • An algorithm that can identify whole sentences.

Apache OpenNLP may be used for the following purposes:

  • Tokenization
  • An example of sentence fragmentation
  • Point-of-sale (POS) tagging
  • Knowing what things are
  • Recognising natural language
  • Chunking
  • Parsing.


C++ and Java are both used in the development of Unstructured Information Management Applications (UIMA), a framework designed to offer a reliable platform for creating software applications. UIMA was created in collaboration by IBM, OASIS, and the Apache Software Foundation.

By streamlining the analysis engine that identifies entities, Apache UIMA makes it possible to transform unstructured data into organised information. Furthermore, it provides a wide range of tools for packaging components as network services.


R is a powerful and versatile programming language that is widely used for statistical analysis and has found applicability in natural language processing and learning analytics. Through its significant impact on big data research, R has become an invaluable tool for data scientists and researchers alike.

Two of the best R libraries for natural language processing are as follows:


The R package ggplot2 is widely recognised for its efficacy in data visualisation tasks. By leveraging the “grammar of graphics” approach to visualisation generation, this package enables users to observe and appreciate the relationships between the visual representation of data and its properties.


By utilising R and the knitr package, it is possible to generate dynamic reports. This process, known as literate programming, enables the integration of R code within a variety of markup languages, such as HTML and Markdown, thereby enabling the creation of dynamic research documents.

Python is a widely preferred language for natural language processing (NLP) due to its comprehensive library support, comprehensible syntax, and its ability to easily integrate with other languages. For developers interested in entering the field of NLP, Python is an excellent starting point because it makes the learning process simpler and more straightforward.

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