Part-1: Introduction to Natural Language Processing (NLP)

Wakeupcoders
Jun 6, 2020 · 5 min read
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Introduction to NLP

What is Natural Language Processing (NLP)?

Natural language processing (NLP) is a field of artificial intelligence in which computers analyze, understand, and derive meaning information from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.

NLP is characterized as a difficult problem in computer science. Human language is rarely precise or plainly spoken. To understand human language is to understand not only the words but the concepts and how they’re linked together to create meaning. Despite the language being one of the easiest things for the human mind to learn, the ambiguity of language is what makes natural language processing a difficult problem for computers to master.

Open Source NLP Libraries

These libraries provide the algorithmic building blocks of NLP in real-world applications. Algorithmia provides a free API endpoint for many of these algorithms, without ever having to set up or provision servers and infrastructure.

Applications of NLP:

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Application of NLP

Embracing NLP in your business is a lot easier once you understand the tangible benefits instead of the abstract potential. With that in mind, here are a few ways NLP can elevate your bottom line over the next few years:

Optimize customer service: NLP optimizes customer service in two ways. First, it lets companies elevate their service levels by providing answers faster (through the web, chatbots, or voice applications like smart speaker apps), working in multiple languages, and handling higher-level questions. At the same time, NLP allows companies to spend less on human service agents, office spaces, phones, and other costs. With NLP, delivering exceptional service has never been easier or more affordable.

Why Natural Language Processing?

Large volumes of textual data:

Structuring a highly unstructured data source:

Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there hundreds of languages and dialects, but within each language is a unique set of grammar and syntax rules, terms, and slang. When we write, we often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter, and borrow terms from other languages.

While supervised and unsupervised learning, and specifically deep learning, are now widely used for modeling human language, there’s also a need for syntactic and semantic understanding and domain expertise that are not necessarily present in these machine learning approaches. NLP is important because it helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics.

Our Real-Life Examples:

Feel free to use our github repo associated with this NLP series : https://github.com/wakeupcoders/Natural-Language-Processing-

Quick Links

Link : Part -2 : Tokenization

Hope you enjoyed this blog. Please join us on Instagram, Twitter, Linkedin This blog is a collaboration with Sambhav Jain. Thanks for reading.

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Wakeupcoders

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We let make your business smarter and broader through the power of the internet. Researcher | Web developer | Internet of things | AI | www.wakeupcoders.com

Analytics Vidhya

Analytics Vidhya is a community of Analytics and Data Science professionals. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com

Wakeupcoders

Written by

We let make your business smarter and broader through the power of the internet. Researcher | Web developer | Internet of things | AI | www.wakeupcoders.com

Analytics Vidhya

Analytics Vidhya is a community of Analytics and Data Science professionals. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com

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