When we talk about the application of NLP (Natural Language Processing) we are identifying one of the most promising areas in the macrocosm of Artificial Intelligence. Indeed, Natural Language Processing is by far one of the most fertile fields of study, research and application, and can provide the most enterprising companies with a huge competitive advantage through the creation of advanced conversational systems. Not only that: given the possibility of integration in consumer products and solutions, the application of NLP could be the basis for a new way of managing everyday life for each of us. NLP could really change the world, and it is no coincidence that the most recent projections indicate a market of more than 43 billion US dollars by 2025 (source: Statista).
What are NLP applications?
Explaining what is meant by the term Natural Language Processing is not at all easy: in fact, the acronym NLP includes various techniques and processes that are however united in order to allow the machines to “understand” the natural language, that is the human language. NLP therefore allows the creation of conversational systems, man-machine communication interfaces that lend themselves to truly revolutionary implementations: consider virtual assistants like Alexa, Siri or Google Assistant, automatic translators, the millions of chatbots which fill Internet pages and Social Media to overflowing, up to more specific – but no less decisive – applications, such as interpreting sentiment from a telephone conversation with a bot, or automatically organizing e-mails according to their content, and so on. Machines can, as a matter of fact, receive ‘natural language’ in the form of voice or text messages.
Challenges to overcome: between linguistics and computer science
All these NLP applications, as well as many others, are feasible as long as the machine is able to carry out a careful work of ‘interpretation’ of the human language, an activity that goes far beyond grammatical or syntactic analysis and comes to understand semantic relationships, or the meaning of words in relation to their context. Only in this way can a chatbot give a coherent answer to a question, or understand the mood of the interlocutor from the words he is using, and then – in case it detects a certain tension – hand the task over to a human colleague for case management. Being a point of contact between linguistics and computer science, NLP encounters various hardships on its way, from the usual semantic ambiguity to terms with more meanings, not to mention dialects and idioms. Moreover, textual and vocal data are not structured and therefore require advanced technologies (a very important contribution is provided by deep learning) to be then used and “understood” correctly. The amount of available data, which has been constantly growing for the past few years, instead works in favor of NLP, which already offers very important practical applications, in addition to being an ever-evolving process and technology.
NLP applications for companies
Despite being the most well-known demonstrations of NLP, let’s leave aside Alexa, Siri and Google Assistant for the moment, to focus on decisive use cases for companies. Chatbots – which have become one of the main technological trends in recent years thanks to their versatility – are definitely the main area of development: chatbots can be used to simplify customer service management, but also for the company’s internal help desk, as support for human resources, and for IT, which often has to handle routine requests such as resetting passwords. Companies that use chatbots, regardless of their sector, experience an immediate reduction in spending, which in some cases goes hand in hand with a significant increase in revenues: in finance, for example, it is possible to create virtual financial consultants, the so-called Robo Advisor, whereas in manufacturing it is possible to manage machinery with voice or text commands. In the retail environment, chatbots can be extremely advanced personal shoppers, able to suggest the best purchase according to one’s tastes.
NLP applications: chatbots as Personal Shoppers
A very interesting use case is to create virtual assistants that act as real “personal shoppers” both in e-commerce and in a physical space. Currently in e-commerce there are a multitude of products for sale with increasingly numerous and customizable features. Thanks to the high level of language comprehension and the use of new generation systems to interact with users (Human Machine Interface), it is now possible create a new online shopping experience that allows a natural conversation with an assistant ready to answer questions about products, how they are made, how to use them, about similar products. A shopping experience that can start with an online virtual assistant and end with a purchase in a physical store with a continuum where online and offline meet, replacing the classic online purchase process, made of cold clicks, with a more “human” approach, in which even the buyer’s words and tone of voice can be used to assess the emotion the buyer is experiencing in choosing that particular product.
NLP in the field of banking cybersecurity
The use of NLP applications is sure to impact the field of banking cybersecurity as well, and specifically to detect zero-day vulnerabilities, i.e. vulnerabilities for which no final patch is available yet. To respond to a zero-day attack, analysts usually study conversations on the deep web, looking for information that is useful for the purposes of the patch. For this reason, an NLU (Natural Language Understanding) engine has been developed, trained with various conversations and relevant documents: this, which acts as a crawler – a search engine – is able to automatically classify all the textual data that it considers relevant, for the purpose of not only implementing the patch against an attack that has already occurred, but also to pro-actively prepare for any unwanted future attempts to access its systems.
The Chatbot enters the factory with NLP applications
The man-machine dialogue in a natural language via chatbot finds areas of application also within the factories, particularly on the production lines but also between the lines and the management applications. In this context Exprivia | Italtel has also developed a specific solution – Indychatbot – which allows an operator to ask machines for the information they need through a tool very similar to common instant messaging applications, using natural language. This not only makes interaction easier but also significantly reduces the training time required to use certain machines. In essence, the operator converses in a realistic way with the machine, which guides him to carry out the most complex operations and can also create work groups which include people, machines and management.
