A decade ago, we couldn’t have dreamt that someday we would be interacting with machines as though they were human. Natural Language Processing (NLP) is changing the way humans interact with machines owing to huge advances in machine learning.
NLP tools such as Siri, Google assistant, Cortana, Amazon Alexa and Bixby have shown us that machines can hold a reasonably intelligent conversation with humans. NLP techniques enable machines to detect and identify patterns in human languages in order to carry out tasks such as speech recognition and sentiment analysis.
Why is NLP Important?
Through NLP, non-programmers are able to obtain useful information from computing systems. From an enterprise’s perspective, NLP has the ability to obtain useful insights from videos, emails and other unstructured data. In recent times, one of the major advantages of NLP for enterprises has been the way firms can interact with new technology and processes in a natural way, without the need for training, enforcement, and hand holding.
In addition to making machines understand and communicate, the use of NLP is advancing towards helping organizations make better decisions, be it by understanding the context of their queries, or by giving a clearer view into consumer behavior.
Let’s take a look at the ways NLP has helped the enterprise make better decisions over time:
Voice Search and Reporting Automation
NLP can help enterprises obtain answers to their business queries by querying software in natural language. This allows them to consider customers’ needs and to provide the answers they need in natural language and in turn helps to create an emotional connection and build better relationships with customers. Brands can differentiate themselves by combining IQ and emotional intelligence via NLP.
With regard to Reporting Automation, NLP applications can be used to rapidly extract meaningful information from unstructured data using a number of techniques. NLP technology can parse any text data to quickly identify the following:
- Keyword relevance and occurrence
- Recognizing named entities
- Tagging parts of speech
- Sentiment analysis
- Relation extraction
- Categories & concepts
- Dependency parsing
Advanced reports generation modules can be created to generate customized reports and dashboards.
NLP can be used to extract insights from customer interaction on social media. Sentiment analysis involves extracting insights from customer’s text and emoticons to obtain an understanding of customer’s attitudes and responses. Specially curated lexicons are used in machine learning to enable machines to analyze sentiment.
NLP can extract data in the form of text, images or infographics and deliver the analyzed information in a decision-enabling format. The following are a few use cases that illustrate the vast use of information extraction using NLP:
- Voice of the Customer (VOC): VOC sources can be call center logs, emails, and social media streams. NLP information extraction works with all data sources to enable enterprises to intercept potential issues before any official complaints.
- Lead Generation: Taking timely action on social media insights can help gain new customers. NLP information extraction can analyze a vast number of tweets, flushing out potential leads and bringing them to marketer’s attention.
Making BI More Insightful
NLP has the potential to democratize data by making BI more insightful. Imagine, instead of scanning through a hundred reports, being able to just ask a relevant question to a virtual analyst. You’ll get all the data that you need. NLP will also make it easier to access data as the need for a UI is eliminated. The processing is taken care of in the cloud and queries can be made by voice on smartphones and handhelds.
You can ask your virtual analyst about customer sentiment for your brand today, all while shopping or walking. NLP is currently based on transforming natural language into machine language. As the technology matures, our computers will get better at understanding our queries and deliver answers rather than search results.
The Future of NLP
According to Analytics Insight, IBM’s Project Debater uses AI technology to actually debate complex topics with people. This at once represents NLP’s potential in all areas of human society. As the NLP technology develops over time, it will be able to understand the subtle complexities of human language as well. Elements such as humour, satire, irony, cynicism and sarcasm will become parts of NLP’s repertoire, which might lead to greater strides in machine-human interaction.
NLP has seen a number of successes in digital marketing, banking, healthcare and other industries. For example, A Nuance–Swedbank case study explains how the bank’s NLP assistant Nina helps customers obtain answers to their queries in natural language. Before the integration of Nina, Swedbank’s customers used to search for the answers to their queries themselves. Now, Nina can handle 350 different customer questions and answers.
In the future, there will be a greater use of chatbots especially in banking, retail and hospitality. Integration with cognitive technologies and semantics will enable chatbots with a deeper understanding of human language.
In the future, NLP will also support invisible human communication, both in text and conversation. An example of this is Amazon’s Echo, which is making a move towards putting humans into direct contact with technology. We can also look forward to smarter search via NLP, or a ‘search as you talk’ functionality. Google recently announced that Google Drive is now enabled with NLP capabilities that allow users to use conversational language while searching for documents and content.