Why students should learn Natural Language Processing (NLP) with Python

As technology continues to grow and govern every aspect of our lives, there has been an increasing traction amongst students to learn more about up and coming technologies. Against this backdrop, Natural Language Processing (NLP), which is also known as the subfield of Artificial Intelligence (AI), has witnessed a significant surge given its ability to deal with the interaction between humans and computers through natural language. NLP has opened new doors to understanding, analysing, and generating human language in a virtual age by its potential to develop algorithms and computation models that are capable of doing so.

Being a multidisciplinary approach, NLP draws on techniques from Computer Science, Linguistics, Maths, and Psychology. At its core, the intention is to build systems that are capable of processing and understanding human language that, being a nuanced form of communication, involves multiple layers of meaning. As a result, industry professionals prefer using Python to manipulate human-generated language data, both speech and text.

Importance

Python’s simplicity enables users to focus on NLP rather than the programming language details, while its efficiency facilitates the swift creation of NLP application prototypes. Backed by a high level of abstraction, and easy-to-read model, Python makes it easy to produce and test codes. In addition, its accessibility and strong community support make it an ideal choice for those striving to build NLP systems. Python and its libraries and tools are especially useful to address problems specific to NLP projects.

While Python’s transparent semantics and syntax make it a great choice for NLP projects, there is also the advantage of solid integration support with other languages and tools to build ML models. Python also boasts a variety of NLP tools and languages, which enables students to manage different tasks like sentiment analysis, text summarisation and chatbots to language translation with ease.

iStock/ Getty Images

Apart from enabling seamless interaction between humans and computers, and powering chatbots, virtual assistants and ML translation systems, NLP helps extract meaningful information from unstructured data via sentiment analysis and text summarisation. Further, NLP algorithms can help derive patterns and insights from analysing textual data, which proves to be valuable to conduct market research, social media analysis, and collect customer feedback.

In addition, NLP automates tasks like answering queries, categorising documents, and producing reports, which enhances efficiency and accuracy across various industry operations. By understanding user preference and behaviour from language usage, NLP can help curate personalised experiences and targeted advertisements, thereby boosting customer trust and loyalty, especially in a consumer-centric space.

NLP libraries

Amid the plethora of NLP libraries offered by Python, Natural Language Toolkit (NLTK) is one of the most powerful supporting operations like classification, tokenisation, parsing, tagging, sentiment analysis, and more, while serving as a strong foundation for developers working on MLP and ML projects. Although spaCy is a relatively new open-source library for NLP tasks, it offers the fastest parsing in the market. Known for its high performance and efficient processing of large text data, it is also poised to support more languages in the future. TextBlob is necessary for students starting their NLP journey in Python, as it offers help to learn basic operations and is useful to design prototypes.

NLP with Python is actively reshaping the way we interact with technology. As it continues to drive innovation across diverse industries, students who have knowledge of NLP with Python can boost their career trajectory by foraying into new domains such as Data Science, AI Studies, and Computational Linguistics.

The writer is CEO and Co-Founder of edForce.