In the age of digitization, we have access to a lot of unstructured data in various forms. From simple audio files to social footprints, there is a lot of data that is generated by day to day operations of an enterprise. Natural Language Processing is one of the best methods of simplifying unstructured data.

What is NLP?

NLP focuses on how human language interacts with computers, and it is a mix of AI, computer science and computational linguistics. It is essentially a smart way to understand and analyze data generated by human interactions. Since human language has a lot of variation in it and while computers can understand human words, it cannot understand how words are linked together to create meaning. Language is highly ambiguous, and the use of NLP makes it easier for computers to understand data pertaining to human languages.

How developers can use NLP?

The use of NLP relies on machine learning algorithms. There are no hand-coded rules when it comes to NLP, and it is all about automatically learning rules by analyzing examples. Natural Language Processing systems become more accurate over time as more data becomes available.

Text Summarization:

Natural language processing can be used to identify important information from text while ignoring any kind of unimportant information. Summarizer is an existing NLP solution that is being developed that you can try out to see a real-life example of NLP being used.

Chat Bots:

Deep learning models and NLP can be used to create chatbots. Chatbots have become fairly common in a myriad of websites, and well-developed chatbots are capable of understanding the nuances of human language very accurately.

Automated Keyword Tagging:

NLP-based tools like AutoTag can be used to identify topics or keywords from a body of text. Latent Dirichlet Allocation (LDA) in Python is a technique that’s used by NLP systems to create automated keyword tagging tools.

Named Entity Recognition:

NLP can be deployed to identify the type of entity from given data. It can process names of entities and connect them to places, persons or organizations.

Sentiment Analysis:

NLP can be used to analyze strings of text and identify the emotion behind it. The analysis tools created using NLP can be used to identify positive, negative and neutral emotions.

Practical examples of NLP

Autocomplete and predictive typing:

Autocomplete is a feature that has been used in digital keyboards, email and in other services for quite some time now. AI is used to predict subsequent words that a user is going to type and suggests words and phrases that may complete sentences quickly.

Spell Checkers:

NLP can be deployed to spell checkers to identify incorrect words and suggest corrections. Autocorrect is another tool that utilizes NLP to fix words when using digital keyboards and word processors.

Digital Assistants:

Digital assistants like Alexa, Siri, and Google Assistant are capable of understanding semantic language and breaking them down to simple commands.

Spam Detection:

NLP is widely used across a number of email services to identify spam and sort them into a separate folder.

Conclusion

NLP is slowly being integrated into more enterprises, applications and digital services than ever before. Natural Language Processing is becoming very important for machine-human interfaces and will be used intensively for analytics as well in the future.


About the Author

Noman Avasthi
Technical Architect @ Asentech LLC
Software Developer, Rambler, Research Enthusiast
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