Natural language processing, or NLP, is an umbrella term for all technologies that help machines take input from humans in their natural language instead of machine language instructions, as well as those that can process unstructured data and draw meaningful and valuable insights from it. The need and scope of natural language processing is clear enough; it can help democratize data science, enabling humans to easily access, understand, analyze, and leverage data, apart from helping them offload data-heavy tasks to machines. Also, NLP can extract massive data out of the thousands of TBs of audio, video, and scanned content archived in several databases. The big question is — how can natural language processing add value to enterprises?
NLP technologies today are smart enough to transcribe and analyze the massive recorded call data that enterprise databases contain. The most prominent applications of NLP are in empowering customer support. This is done with the help of instant messaging applications that can interpret inputs in natural language, conduct quick analysis and search, and provide outputs in natural language. Also, NLP technologies are used in virtual assistant software that can help solve quick queries, for customers and employees alike. This can significantly reduce the load on customer service personnel, and enable them to work on more value-adding activities such as business development. Speech recognition and machine learning have helped bolster NLP technology-based applications in a lot of ways, and the abilities of these applications are going to expand with time.
Enterprises realize that irrespective of the market they operate in, their reputations are dependent to a great extent on the kind of “sentiment” being expressed about their brands online. Social media platforms have become ultra-important; consumers actively participate in reviewing their brand experiences and posting unsavory instances with businesses. With so much happening “outside” the information network of an enterprise, the need for some sort of reputation management applications becomes obvious.
Among these, sentiment analysis tools are the perfect example of how NLP technology can help solve enterprises’ problems. These applications are able to analyze content across social media platforms and tell you the sentiment being conveyed about your brand — positive, negative, or neutral. With real-time updates available in dashboards, you can mobilize your resources to leverage positive sentiments and take care of the negative sentiments by careful and empathetic responses.
Did you know that many of the leading tech companies in the world earn most of their revenues via advertising? For every enterprise, advertising budgets constitute a significant portion of their marketing expenses annually. Any sort of targeting can help them channel their resources in the right direction.
Traditionally, enterprises have relied upon demographic (age, region, gender, etc.) variables and psychographic (values, opinions, beliefs) variables to segment their markets for targeted advertising. However, the true identity of people is best captured in their search engine browsing and social media activity. This is where NLP technologies pitch in. By identifying patterns in unstructured data spread across several web platforms, NLP technologies can segment users into highly nuanced groups, called personas. These personas, then, become the driving mechanism for highly targeted advertising. Not only does this help enterprises drastically reduce their advertising costs but also delivers significantly higher ROIs than usual.
Enterprises need to track and monitor information exchange channels in the market. Shareholders, investors, government, employees, competitors — there are way too many stakeholders in the information exchange ecosystem. Then, you’ve got RSS feeds, industry journals, niche newspapers, press releases, government portals, public information databases — that’s way too many information channels to track and monitor. How does an enterprise make sure that it remains intelligent, even with so many elements in the information exchange ecosystem? Natural language processing helps.
“Event extraction” is an NLP technique that parses information to mine information about specific events. Mergers and acquisitions, key takeovers, changes in the board of directors, key job role changes — any kind of event can be identified by an NLP algorithm.
This can create a structured database of event information about companies, which is invaluable for an enterprise. Hedge fund companies are already using this NLP technique to improve their buying and selling algorithms.
Another technique, called sentence classification, allows NLP algorithms to classify statements with certain tags. For instance, a company’s CFO could say something as naturally worded as “we’re targeting double-digit growth in sales during Christmas week.” An NLP algorithm that uses sentence classification technique can then classify this as a forward-looking statement.
Product research and insights
Unstructured data is a massive source of valuable insight for enterprises, helping them understand customer reactions and opinions about products, and, for example, the most desired product features. The pharmaceutical industry is a great example, where products are researched for possible adverse side effects after they are launched and marketed. Here, NLP can help them extract a lot of useful information from blogs, social media posts, and search engine queries, apart from public health records and structured information sources. Named entry recognition and relation detection are the two key techniques that make NLP algorithms capable of extracting information from sentences where a drug, medical symptom, or disease is mentioned and identify the context to associate cause and effect.
Underlying characteristics of all these applications
In the coming years, these underlying aspects of natural language processing will ensure wider adoption in enterprises:
- Focusing on reducing the gap between human and machines in terms of communication.
- Bringing process automation and operational efficiency improvements.
- Pushing the barriers of data analysis by bringing unstructured data into play.
- Helping humans automate redundant and data-centered tasks.
- Extending the capability of existing business intelligence assets in the enterprise.
Natural language processing: Bridging humans and machines
It’s clear that any natural language processing brings massive improvements in human-machine interactions, and augments machines’ capabilities to capture and analyze a wider range of data formats. These core elements will help drive NLP applications.
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