If humans are becoming more comfortable interacting with AI and voice assistants like Google Home, Echo and Alexa, it is largely due to the advances in Natural Language Processing (NLP) and Natural Language Generation (NLG). NLG, in particular, is becoming popular with companies for tasks such as crunching financial data to help them make informed decisions.
What is Natural Language Generation
NLG, NLU (Natural Language Understanding) and NLP are the subsets of Artificial Intelligence (AI) that are critical in human-computer interaction. Natural language generation is defined as the task of generating written or spoken narrative from a set of data. NLG essentially translates raw data into easily understood text or spoken word for humans to comprehend.
NLG applications fall under two categories: template-based, rules-driven NLG and advanced NLG that relies on machine learning. Advanced NLG uses neural networks that learn lexical and grammar patterns from vast troves of historical data to produce more intelligent insights. Programming languages that are useful in NLG include Python, Java, R and MATLAB. Programmers also make use of awk/gawk as data extraction tools along with other common NLP/NLG tools like Gensim and Stanford CoreNLP.
Businesses that want to introduce an NLG component to their data stream can start with NLG platforms and tools available. These tools use advanced NLG engines to scan through the key information in the data.
An Advanced NLG engine usually has two components –Data Analytics & Interpretation and Information Delivery. The first stage gathers data from various sources and extracts insights from it. The second stage of processing in the NLG Engine takes these insights and creates an effective communication plan for it using text and as required, graphical representations of data.
The narrative output can be in various formats – Word, PDF, HTML combined with charts, graphs and/or delivered as speech. This is particularly complex and is called multilevel NLG.
A few NLG vendors also offer tools and platforms that give developers online access to software architecture that they can use to build enterprise-level NLG systems for large corporations. This allows smaller businesses who may not have the budget to hire an NLG vendor to build NLG applications using a structured cloud-based interface.
Business use cases of NLG
Making sense of data
NLG is already being used by the Associated Press to produce quarterly business reports in a fraction of the time it originally used to take. For example, every quarter it must track public corporations that declare their corporate earnings. This task which usually required several hours of sifting through the information is now done automatically with an NLG platform. The NLG technology scans each report, goes through the numbers and derives financial insights which are then translated into easy-to understand language.
With NLG, structured data can be analyzed and communicated with precision, accuracy and can be scaled up efficiently. NLG platforms can run through the data to present the analysis and coherent narratives onto dashboards, so complex statistics can be easily understood by everyone in an organization without requiring human assistance. With structured data, companies can use NLG to partially automate analytics reports and product descriptions.
Making chatbots more useful
This is of particular use to small businesses. They can use chatbots to handle customer queries coming in from across the globe, 24/7 without needing to hire additional staff.
Chatbots are improving with every new advance in NLG. They will no longer be restricted to simple queries but be made intelligent with the help of NLG to engage more meaningfully with the customer and then connect with a human employee if required.
Advanced NLG technology that is synced with the company’s enterprise ecosystem and workflow management can build a strong path of communication between employees and customers and improve productivity.
Improve Inventory Management
Data plays a crucial role in inventory management, supply chain, production, and sales. Store managers are dependent on this data to make decisions about maintaining inventory.
There is always a chance of human error and judgment in this scenario as store managers may not always interpret the data accurately. NLG can automate this task for them, by scanning large amounts of data and presenting them with a clear narrative and analysis. NLG can present them with a report and recommendation on what they need to stock up on for the time frame, thereby minimizing human error.
Today, companies are actively building their data infrastructure to help with business analytics and data-driven decisions. So far, they have relied on automated data collection. The analysis, insights, and communication tasks are the ones that are prone to errors. NLG can help reduce those errors to a minimum, streamline the process and help businesses with their decision.