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Natural Language Processing with Python: An Overview

Natural language processing with python

Is natural language processing with Python the easiest way to get started in the field of NLP?

Python, a generalist programming language, offers a wide range of libraries, packages, and toolkits that can be used for a wide range of functions, including natural language processing. in this article, we will take a look at the basics of using Python for natural language processing period

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Natural Language Processing with Python: A Crash Course

Natural language processing (NLP) is an AI field that focuses on the automation of human language processing. 

NLP performs tasks such as:

  • Analyzing and parsing text
  • Analyzing spoken language
  • Extracting characters from written language
  • Generating new text

All of these functions, in turn, can be used for advanced NLP-driven apps, such as chatbots, voice user interfaces, and text generation tools.

Python is one of the best programming languages for NLP, whether you are just starting out or whether you are interested in developing your career as a programmer, since the language itself is easy to learn and it includes a robust set of NLP tools.

Let’s look at a few of those now.

Natural Language Toolkit (NLTK)

NLTK is the go-to package for developing NLP applications with Python. It is relatively easy to use and learn, making it an ideal starting place for anyone interested in NLP, AI, and machine learning.

This package can perform all of the key techniques in NLP, such as:

  • Tokenization
  • Stemming
  • Lemmatizing
  • Chunking
  • Named entity recognition
  • Sentiment analysis

Also, when used in conjunction with Python’s other AI packages, you can develop very sophisticated NLP applications. Although these are not technically required, the added functionality can be useful for data science, machine learning, developing fully functional software programs, and more.

It is also worth noting that although NLTK is useful for NLP, it is not always used in industrial-grade applications. For many applications, it is not quite fast enough for the demands of large-scale use, so other toolkits and programming languages are often used instead.

That being said, given its wide set of features and its ease of learning, this is probably the best place to get started for those new to this field.

Other Useful Python Packages

Besides NLTK, NLP programmers will most likely want a range of other packages that are suited to data science, such as:

  • NumPy
  • The SciPy library
  • Matplotlib

Since NLP is quite a technical field, these other packages offer the capabilities needed to perform in depth analysis and manipulations of data.

Those using Python for more intensive AI applications will also likely want AI packages, such as PyTorch, an open source machine learning framework for Python. This framework can be useful for developing machine learning applications, but, as mentioned, it is not the only option for NLP.

Beyond Python: Industrial-Grade NLP with spaCy and Cython

Cython is part Python, part C. This is the core engine of spaCy, add language frequently used for NLP.

Since Cython is so similar to Python, the syntax is not difficult to learn. Once you have a handle of Python itself and NLTK it should not be difficult to pick this one up.

Since part of the language is lower-level then Python, it executes faster and can be very useful for industrial-grade NLP applications, as mentioned. 

Any programmer working in an enterprise setting who needs speed should consider learning a faster language such as this one.

That being said, if speed is not an issue and you are not intent on becoming an NLP practitioner, learning Cython may not be relevant. The performance gains become important for large-scale applications.

Beyond Python

Python is certainly one of the most user-friendly languages for developing AI and NLP applications, but it is not the only one.

Others include:

  • Java, a longstanding, low-level language that can run on a range of platforms. It includes a range of integrations and features that make it easy to develop robust AI, NLP, and data science applications.
  • R is a language developed specifically for data science applications. Although it is most useful for statistics and data science, it is also used for NLP. For those interested in developing data-heavy applications, R is worth considering.
  • Prolog, short for logic programming, is useful for creating chatbots. Although this language has been around quite a while, it focuses specifically on logic and linguistics, making it useful for certain types of NLP applications. 
  • Lisp is another older programming language that was one of the most popular choices for AI and machine learning projects. Despite its age, it has continued to evolve over the years and is still used widely today.
  • Haskell, a functional programming language, has a small but ardent following of developers. Those with experience in a variety of languages interested in trying a language that is considered elegant an advanced may consider testing this for certain projects.

In short, there are several programming languages that can be used for NLP. Although Python is the most common, it is certainly not the only one.

In general, those new to NLP often start with Python, and for good reason. As we have seen, it has a range of packages and functionalities, a passionate community of supporters, and it can be used for applications that are slow, fast, simple, or complex.

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