No matter what technology domain you are looking at, the unanimous expectation of the next year is that natural language processing will take the leading role in this domain advancement. Be it Business Intelligence, FinTech, or Healthcare, NLP seems to become a field-shaping technology.
In 2018, businesses used NLP techniques in several areas:
In the near future, the importance of NLP for businesses is expected to constantly rise. But why has NLP moved from a relatively marginal technology to the forefront of AI? There are many reasons.
First of all, it is related to the fact that industries tend to switch their customer communication from humans to chatbots, and finally, the newest trends in natural language processing and sentiment analysis have made it possible to create viable chatbots that would assist and not irritate customers. Gartner predicted that in 2018 (and beyond), NLP would be combined with Machine Learning and Big Data to build powerful question-answer systems such as chatbots. In 2019, chatbots, especially contextual ones, smart assistants and conversational AI will allow organizations to drive digital transformation across people-related spheres, including customer support, sales, marketing, human resources, IT help desk, supply chain, payroll, and many others.
Natural Language Processing (NLP) as the sub-branch of Data Science is the field that attempts to extract insights from something that can be viewed as a “text.” In NLP, machines are taught to read and interpret the text in a similar human manner. Such close-to-human capability for interpreting text is crucial for analyzing large volumes of text data. As NLP continues to make “data” more user-friendly and conversational, more and more mainstream users will adopt NLP-driven Data Platforms. Let us hope that with the advancement of NLP, the differences between natural language and machine language will be blurred. In a way, NLP will remove the current barriers to entry for Big Data BI. Someday, business users may engage in BI tasks through “conversational” interactions with smart assistants or chatbots.
The application of machine learning to natural language-related tasks has been always beneficial. As SAS Artificial Intelligence and Language Analytics Strategist Mary Beth Moore points out, machine learning and natural language are the foundation of any AI system. In 2019 this tandem will make enterprise AI more accessible and acceptable to laymen users not necessarily aware of their impact affecting an array of business objectives.
Though a part of AI, we think that Deep Learning deserves a separate point for its predominance in present-day technologies. Though until recently, it was chiefly used in image recognition, Recurrent Neural Networks are now beginning to gain traction in document classification and entity tagging. When used in conjunction with natural language, deep learning provides higher accuracy in sentiment classification, document classification, and abstractive summarization in comparison with machine learning techniques.
Deep learning is paving the way for conversational speech recognition systems trying to achieve a more realistic experience of human-machine interaction. One aspect of this new trend is building speech-to-text user interfaces that involve audio recognition, and conversion of the audio into text, which behind the scenes is going back to ones and zeros and the backward movement from the machine binary code to text and even the automated voice. The large amounts of math in this procedure require significant customization of speech-to-text systems. However, they remain critical of making human machines more interactive and responsive to the needs of the business.
Semantic search is another trend that is expected to impact natural language and machine learning in 2019. With the growing document flow, especially in the framework of increasing regulatory measures, organizations require intelligent “semantic” search for specific terms, concepts, and business requirements. Such search goes beyond traditional NLP into the domain of Natural Language Understanding and requires a minute comprehension of the core ideas contained in the text achieved with the help of supervised ML techniques.
Though text analytics will remain one of the most important NLP tasks in the coming year, the future lies in the development of more intelligent applications that tackle the problem of cognitive computing using a variety of technologies, such as deep learning, unsupervised and supervised machine learning and the multitude of natural language technologies. In this context, the NLP will outgrow itself and fall into several distinct categories including Natural Language Processing, focused on linguistics and language’s classification and text semantics, Natural Language Understanding, analyzing the actual meaning of words, Natural Language Generation, dealing with the production and generation of text and speech, and Natural Language Interaction — a conflation of the above-mentioned technologies in which users communicate and evoke responses from systems via natural language. In this form, NLP is likely to become one of the most visible forms of artificial intelligence in existence.
Although NLP technology is still in its infancy compared to other AI technologies such as Neural Networks or Deep Learning, it is gaining more attention from the business nowadays, with a growing understanding that coupled with ML and Data Science it has the potential to replace human data scientists and finally achieve direct human-to-machine interaction.
