Natural Language Generation explained
5 min read
5 min read
We are all familiar with Intelligent Personal Assistants like Siri, Alexa, Cortana, and Google Assistant. These are adept at understanding the context of the query and presenting results in spoken language, sometimes also providing useful links, maps for directions, etc. Such systems are based on Natural Language Processing (NLP) – a combination of computer science, artificial intelligence, and computational linguistics – aimed to help humans and machines communicate in natural language, just like a human to human conversation. An effective NLP system is able to comprehend the question and its meaning, dissect it, determine appropriate action, and respond back in a language the user will understand. An NLP-based Intelligent Personal Assistant comprises four modules, namely Speech Recognition, Natural Language Understanding (NLU), Natural Language Generation (NLG) and Speech Synthesis.
Natural Language Generation is one technology in vogue with the global natural language generation market size expected to grow from USD 322.1 million in 2018 to USD 825.3 million by 2023, at a Compound Annual Growth Rate (CAGR) of 20.8% during the forecast period. NLG systems analyze the given data and generate narratives in conversational language. This technology is being increasingly adopted by enterprises to enhance productivity by automating time and resource-intensive data analysis and reporting activities. The rate at which data is generated is much higher than the rate at which humans can analyze and interpret it to gain insights and arrive at business decisions – as a result of which current market opportunities are lost. This is where an NLG system would save the day! A machine can analyze data and present it to users in a comprehensible manner at an extraordinary scale and accuracy, helping the firm cut down costs, enhance customer satisfaction, improve organizational efficiency and generate more revenue.
vPhrase has been ruling the NLG space with its patent-pending platform PHRAZOR. A leading Indian private sector bank has been using PHRAZOR to do portfolio analysis and generate quarterly reports for its clients. The reports are generated in simple, conversational language helping clients quickly understand their financial health and investment status, as a result of which client satisfaction with the bank has shot up. The platform also provides investment-related recommendations, after close analysis of market performance.
A stock broking company in India has its research team analyze all the companies listed on the National Stock Exchange and Bombay Stock Exchange on a quarterly basis. All the companies listed need to be analyzed on the basis of 35-40 indicators such as Debt/Equity ratio, PE ratio, Market capitalization, EPS, Bid Price, Ask Price, Volume traded, etc. Having fed the performance indicators into PHRAZOR, it churns out easy-to-understand quarterly analysis reports within a matter of seconds.
A prime media company has been deploying PHRAZOR to understand which of its shows have the maximum positive impact on the ratings. Given the performance data, PHRAZOR provides crisp narratives coupled with a statistical summary to highlight the performance of various shows being aired on the channel and their impact on the ratings. It also helps to identify shows not making the cut along with reasons for the same. Information about rival channels is also quickly presented in an easy-to-understand manner, giving the channel a fair idea about where it stands in the competition.
Natural Language Generation has made its presence felt in various domains ranging from banking and financial services to media, healthcare, and education. It is here to stay to help businesses improvise their processes and tread faster on the path of advancement!