Benchmarked by industry forerunners and expanding explosively by its own methodology, the best customer experiences in natural language processing (NLP) are found through continued and correct application: a surprisingly difficult task, given the subtleties of human expression.In the late ’90s, the Internet first emerged as a vast, unknowable expanse. The first companies to address this digital mass – Google, Yahoo, eBay – built broad-based search engines to identify and isolate monetizable elements of this new medium, and to provide a clear map of what was worth seeing and experiencing on the web, based on each search engine's proprietary means of scoring content relevance.Upon analyzing the mass of unstructured data that makes up 80 percent of the user posts, email, messaging and other documented assets that comprise the Internet, businesses are challenged to understand what’s relevant.Natural language processing, along with machine learning, have developed into fast-growing fields that businesses are using to make more efficient decisions.
Almost 20 years after the foundation of Google, Yahoo and eBay, the complexity of question-based queries and the volume of content that the Internet is framed around has elevated in magnitude, but the challenge has largely remained the same. A series of distinctions have been made in computer science to discuss how that challenge is being addressed through the use of intelligent systems.While artificial intelligence builds systems that are broadly capable of acting intelligently, machine learning (ML) is a set of problems therein where those systems are equipped with experience, which is applied independently of other processes, so that they may learn. Machine learning addresses the question of data relevancy with natural language processing (NLP).In NLP, the specific aspects of teaching a system to read and understand text are based in machine learning principles – the system may not necessarily intrinsically understand language, but, given examples of it, may develop an algorithm by which it can process it. These applications of AI have advanced more in the past three years than in the three decades preceding them.
Chatbots and intelligent assistants have been around for decades, and represent the first of continuing strategies for using NLP to interact with users, who may or may not be aware of whom or what they are speaking to. From Eliza, who was built in 1966 parallel to the Turing test to converse with and simulate a human, to Siri and Cortana, who reside in every smartphone and home device, the first demand for machine learning has always been the guide, the helper – the personal assistant who is never unavailable, less than pleasant, or short of answers.Driven by the development of the smartphone and the resulting cloud that represents the Internet of Things (IoT), modern conversational devices seek to digest a wealth of information in order to provide answers to any user who might consult them. Below are a few of the major virtual assistant contributors:
Digital voice-activated devices have also become the standard approach to mobile development, and their market is expected to double YoY through 2020. Gartner also predicts that, by 2018, 30% of our interactions with technology will be through conversations with smart machines. As mobile internet consumption increases, voice and more complex queries are starting to take off. Advances in natural language processing will work along these new vectors, by using the some of the following classes below:
Financial organizations have been first adopters of NLP, chiefly to identify insights within the deluge of digital reportage. To assist their execs with deciphering quality data points, IBM's Watson Knowledge Studio is able to append notifications to specific stocks, read public confidence in a company/new executive hire and evaluate the implications of international news events to provide prompt, actionable advice.Additionally, NLP can also be used to comb social media for positive/negative sentiment in client conversations, alloy analyst reportage with current, detailed information and even find patterns of query and purchasing that indicate insider trades.Watson is also consuming millions of scholarly articles and unifying them into a prognostic and therapeutic model, for use by clinicians to detect and identify heart disease. This process, known as clinical decision support (CDS), maps unstructured data and reformulates it into useful information, for re-admitted into medical records. For this task, Watson processed 21 million records in six weeks, achieving an 85 percent accuracy rate for patient identification. This success geared the supercomputer to tackle the more sophisticated problems found in the indicators and associated genomic data used in cancer diagnosis – taking the potential of Watson beyond speculation, into concrete application in a critical field.
Looking towards future implementations of these powerful algorithms, businesses are increasingly focused on several key features that have shown direct return in locating the voice of the user:
Balancing these features will be vital to keeping consumers engaged as NLP continues to mature.
The route to replicating the best customer experiences in NLP, as already benchmarked by industry forerunners, lies in continued and correct application. Developers would utilize the latest in open source tools, agree on new standards and even grow new languages to suit the needs of complex learning systems.Although there are still many solutions yet to be solved against NLP processes, businesses must drive towards finding the voice of the user and identifying what they are truly seeking. As this new technology matriculates itself into more and more business processes, having a quality partner to assist in these decisions will be crucial in staying ahead of the curve as business becomes more streamlined and automated.