Natural Language Processing & Natural Language Generation: Difference and Applications

The Artificial Intelligence (AI) revolution has potentially changed the landscape of modern technology more dramatically than any other disruptive technology in history. Every other day, AI is making new progress in research as well as application. Natural Language Processing (NLP) has emerged as one of the most important applications of AI. Major firms all over the world are investing large amount of money in new language-enabling technologies. Though, NLP technology has been doing the rounds in the industry for quite some time, related technologies like Natural Language Generation (NLG) has emerged quickly.

Difference Between NLP & NLG

Amidst a plethora of technical jargons, NLP and NLG has earned its share of popularity in recent times. Though the terms are related, they differ in terms of their functionality and purpose.

NLP covers a huge area starting from a machine's ability to ingest data to its ability to respond back in a language legible by humans. The technology helps machines to read and process a language by transforming a text into structured data. NLP was the main focus of research until recent past, when getting data and teaching computers how to read the data was more of a concern. Now that businesses are inundated with data every day, the focus has now shifted to NLG. The problem now lies to actually getting valuable insights from the data.

With NLG, computers turn structured data into readable format understood by humans and thus help machines to write languages. NLG produces a text from structured data after interpretation and analysis. So, NLG system essentially acts like a translator. The concept of NLG has circulated in the industry for a long time, however, the commercial applications of NLG became frequent until recently.

However, before applying NLG in operations, businesses should keep the following points in mind. Firstly, as of now, NLG can be used in specific business cases only. NLG is not evolved enough to be completely independent. So, it will take time for NLG to fully automate processes. Secondly, the data should be well-structured for NLG to run analysis.

With advances in NLG, computers will be able to write texts analogous to a human being. The challenge with NLG now is that it is unable to comprehend the undertone of a sentence. However, with more technological improvement, this problem will soon disappear.

A corresponding colloquial term related to NLP and NLG is NLU – Natural Language Understanding. It is a specific type of NLP and mainly deals with the reading aspect of NLP. The coverage of NLU is much narrower compared to that of NLP. It tries to comprehend the meaning of a text by considering the subtle nuances of a text.

So, basically, NLP helps machines to read language, NLU helps them to understand language and NLG helps machines to write language.

How NLP & NLG Can Benefit Us

Minimal Human Intervention

NLP can automate processes in different industrial sectors by minimising human intervention. This can be revolutionary in case of service industries, where human interaction and intervention is very high. With NLG, computer can also explain the reason behind taking a particular decision. So, NLG and NLP can transform a computer to a smart computer and help to accomplish tasks without much help from humans. It removes the dependence of businesses on manpower.

Interaction with Machines Becomes Easier

With every business getting huge influx of data, streamlining data becomes necessary. NLG comes handy in this case as it converts a cumbersome dataset into legible narratives which can be understood by humans. With NLG application, communication becomes more precise and the accuracy increases. This in turn frees up human resources to be involved in cognitive and creative processes. So, NLP and NLG will essentially change how humans interact with computers.

Smart Management of Business Operations

NLG and NLU can work readily on structured data from various sources to get insights. It can thus lead to automated content creation and delivery of results in expected format. It is a cost-effective technology, so from business perspective, firms are eager to apply NLG and NLU in their operations. Advanced NLG can have far-reaching applications like smart management of inventories. It can make the overall reporting and analysis process much easier to handle and interpret. It can also help to monitor performances across different operations.

Conclusion

With advent of AI algorithms and machine learning, the reliance of businesses on manpower is decreasing and interactions and operations are getting rapidly automated. With advanced NLG, a smarter network will develop that will be more efficient than any human network system. A Gartner study forecast that by 2018, 20% of business content will be authored through machines using NLG and will be integrated into major smart data discovery platforms. So, with increasing service-specific intelligent systems incorporated by businesses worldwide, the importance of NLG and NLP will grow exponentially in future.