From its inception, the Internet has been a massive and constantly expanding conglomeration of unstructured data: articles, commentary, forums, secure networks, assets, and so on. Businesses, however, primarily operate on structured data. So the potential for providing actionable insights from Internet data must be derived through finding some methodology of analysis to structure it.

When the Internet grew into a vast repository of potentially useful data, the concept of processing information as human communication was redefined. Natural language processing — which began as “machine translation” in the 1950s and was focused on automatic translation between English and Russian — truly came into its own in the context.

Natural language processing (NLP) is the ability of a computer program to understand human speech as spoken, and is a subclass of artificial intelligence (AI). NLP asks computers to analyze human language so they can understand how humans speak, and finally draw meaning from human language along pragmatic functions. NLP is mainly used to help people navigate and digest large quantities of information that already exist in text form. It is also used to produce better user interfaces, so that humans can better communicate with computers and with other humans.

NLP is characterized as a hard computer science problem, as human language is infrequently precise or simply used. The activity of human language is not derived from the words alone, but from the concepts they symbolically represent and how those concepts are joined to create meaning. Language is easy for humans to learn, but the ambiguity of its meaning is what makes language much more difficult for computers to semantically parse.

Jumpstarting NLP: The Statistical Advance

AI began a statistical shift in the late ‘80s, which was specifically complete for NLP near the end of the 2000s as nearly all papers concerning NLP after that point are statistically derived. Current approaches to NLP are based on machine learning, the subset of AI which was the primary beneficiary of this shift, and many common NLP applications, such as parsers or automatic translation, are possible as a result. Though used scientifically in blogging, financial news and reports, doctor’s notes, literary works, historical sources, and more, NLP is an engineering function generally focused on developing commercial systems that solve problems with a language component.

Giving the Right Answer

Precise and correct answers to human questions is NLP’s goal, and recent approaches are navigating very closely to it. The ultimate expression and use of NLP is to determine a problem posed in text and give the solution in the same language. Additionally, this entire process must be carried out in natural language to be internally consistent.

This problem, faced by the NLP research community and businesses worldwide, is pragmatically addressed by the text classifier. When given both product data and expert responses to the data set, the classifier’s goal is to naturally reconstruct the expert. The most common example of a classifier in business applications is a recommender system, which makes personalized recommendations based on use data. Recommenders are frequently used in the services of Google, Facebook, Twitter, Amazon, Microsoft, and many other major companies.

Current Developments in NLP

As mobile Internet consumption increases, voice and other complex queries are starting to gain preference among Internet users. Recent advances in natural language processing features include:

  • Automatic summarization calculates the frequencies of words in a body of text, then uses the the most meaningful parts of its context to sort, simplify, and reconfigure the text. In extracting these important sentences and creating comprehensive summaries, the important and most essential aspects of a document are quickly assessed.
  • Sentiment analysis takes the ways in which humans express opinions with language, and refines it into more concise and focused structure with clear meaning, which is done by to gauging the attitude, polarity, mood or intent of the speaker.
  • Q & A is of course the explicit and final priority in addressing a query, employed to funnel all searches into the topical and correct response that most users who make the query are seeking. The algorithm does not need to understand these queries – it must only identify the assets relevant to the query, and then match it against the organizational text that categorizes the correct answer.

Definitive Examples of Business Applications in NLP

  • To provide better customer service, virtual assistants and chatbots have become an essential aspect of the Big Four ecommerce giants’ services, represented by Alexa, Google Home, Cortana and Siri. Cortana, specifically, has been configured for hundreds of major-player applications, while Alexa utilizes a set of Skills to personalize its assistance for thousands of corporations and designers.
  • IBM Watson has gained expertise in clinical decision support through mining data sources and websites to assist doctors in personalizing patient treatment and providing precision medicine and cancer care.
  • Persado generates and tests precision display, Facebook, email, mobile and web advertising, at scale and in real time, using NLP and machine learning algorithms.
  • Financial companies cultivate their market intelligence with NLP, enabling them to learn about specific stocks, monitor company sentiment, and quickly respond to news events.
  • NetBase extracts information in real time from social media posts, using NLP to understand customer perceptions or needs and monitor brand reputation, indicating where direct response to consumers is necessary.

Emerging Strategies in NLP

Businesses who are future-minded in their deployment of NLP are focused on developing the following features:

  • A zero or invisible user interface is a conversation resembling a customer-representative interaction, where the user speaks simply and with minimal abstraction to the machine. The machine uses NLP to understand what the customer says, regardless of how it is being said.
  • Smarter search is a functionality where machine comprehension is based on conversational query rather than underlying keywords or topics, and is often used in GPS-type applications where articulating needs and inquiries is not necessarily done consciously by the user.
  • Better operation in the question answering aspect of NLP involves detecting tone from choices of phrases and idioms commonly used in human communication. Through this methodology, the fabrication and comprehension of business reports, transcriptions, reviews and other audience-specific communications will become much easier and more efficient.

Balancing these features will be vital to keeping consumers engaged as NLP continues to mature.

Facing The Future of NLP

The state of deep learning tools, such as neural networks, show promise for applications within NLP, and within these complicated systems, there are now team-like models where AI’s are already creating their own languages for internal communication while learning our own.

Gartner predicts that, by 2018, 30 percent of our interactions with technology will be through conversations with smart machines and, by 2020, 85 percent of customer interactions will be managed without human intervention.

As the issues ahead that NLP will address, and overall methodology continues to improve, having a guide into NLP’s rapidly refining world will prove essential. Any organization seeking to participate in these interactions must prepare to shift into a more streamlined and automated future if they hope to take advantage of what these new technologies have to potential to bring.