How does Natural Language Processing apply to IoT?

By: A. Tewari

The Internet of Things (IoTs) has a deep connection with artificial intelligence. IoT systems generate large amounts of data, and data is the core of artificial intelligence and machine learning. At the same time, with the rapid expansion of connected devices and sensors, the role of smart technology in this field is also growing. Nowadays, the application of computer intelligence in IoT products varies as per the requirements. This article focuses on a specific area of ​​artificial intelligence, Natural Language Processing (NLP). One of the core concepts of natural language processing is the ability to understand human speech. Without NLP, it is impossible to implement voice control on different systems. In IoT, it is difficult to overestimate the value of speech recognition. The hands-free voice interface can bring many benefits to the IoT environment. In some cases, this is just a usability issue; the more complex the system, the harder it is to implement a user-friendly mobile or web interface to control it. In turn, the voice interface is intuitive in nature and does not require a serious learning curve.

In the consumer market, the popularity of voice control is also increasing. About 50% of American households use voice to access online content. Therefore, increasing the number of smart consumer electronic products activated by voice has become a natural step in technological evolution. In addition, NLP not only enables us to integrate speech processing into devices and sensors. Due to the machine translation function, it enables the localization function. With the level of market globalization that we are experiencing today, localization even goes beyond translation and unleashes the benefits of transcreation (creative translation). If the product is focused on cross-border distribution, machine translation is invaluable for any IoT product that enables voice recognition. However, the value of translation function itself is not a lot [1].

Figure 1. Voice Control Using NLP and IoT

Speech recognition is closely related to another NLP concept: question answering system, which is self-explanatory. Question and answer tasks allow us to determine answers to questions given in natural language. Nowadays, more and more devices that support voice recognition use question and answer to provide feedback for user input. The most common examples are popular home assistants such as Amazon Alexa, Google Home etc. These devices are activated and controlled by voice and can answer various questions. Therefore, voice assistants can help people quickly obtain relevant information on the go, thereby improving user work efficiency.

Sentiment Analysis

Customer service can also use sentiment analysis. It can even eliminate invalid investigations. Instead, smart concierges can ask customers questions about their experience and automatically determine their satisfaction. In general, these functions can not only create a competitive advantage for enterprises, but also provide customers with personalized products and services. In addition, due to sentiment analysis and trend monitoring, various connected devices can finally find answers and provide products and services that consumers need or want. The Internet of Things not only connect things, but also connects technology. Imagine a world where devices work with humans, understand their queries, feel their needs, and provide relevant responses. At this point, only by improving artificial intelligence and NLP can such a world be realized-these technologies can achieve contextual understanding and allow smart devices to truly solve our problems [2].

Understanding APIs

Cognitive APIs are perhaps the most important component of natural language processing, text and video and picture processing to assist consumers in understanding the capability of the voice commands with Watson IoT. The Watson APIs for IoT assist with speeding up the improvement of intellectual IoT arrangements and administrations on Watson IoT. Utilizing the Watson APIs empowers users to fabricate intellectual applications that include [3]:

  • Natural language processing (NLP): enabling clients to collaborate with frameworks and gadgets by utilizing straightforward human language.
  • Video and picture processing: enabling clients to screen unstructured information from video feeds and picture depictions to recognize scenes and patters in video information.
  • Text processing: enabling the mining of unstructured text-based information, including records from client calls at a call place, support methods and investigating, professional upkeep logs, blog remarks, and tweets.

By interfacing the client, the text processing API empowers mining of unstructured literary information coming from sources like records from client call focuses, upkeep specialist logs, blog remarks, and tweets. There are numerous things that should be possible today utilizing the innovation accessible in mix with regular language handling which are something other than making an interpretation of voice into text. Some APIs additionally offer the capacity to get text, semantic, which means, notwithstanding the subtleties related with minor departure from how individuals are asking things.

NLP- A Bridge between Consumers and Frameworks

NLP can assist with working with communications among clients and frameworks. Currently, the common practice to communicate with a framework is through a UI, either a UI in a versatile application, a program or a control place, where a singular press fastens and clicks a bunch of switches. What makes things diverse with Natural Language Processing (NLP) is the means by which an individual can conjure this activity.

NLP adds the option of an elegant ‘exchange’ to be utilized with similar gadgets, providing orders verbally or questioning them for status and issues. This can be envisioned with a support expert attempting to investigate an issue. The Internet of Things (IoT) is the thing that makes this conceivable availability, robotized information assortment, connection with different information source, like climate, and more installed handling power [4].

Curiously, the bits of knowledge created by existing APIs can be utilized again to retrain the framework, empowering the client to additionally work on the exactness and practicality of the data produced going ahead. Moreover, the APIs of the stage can be joined to acquire data another organization, like sound, into the text information base for Text Analytics. Frameworks are accessible for changing over discourse into text and text into discourse utilizing the Natural Language Processing (NLP) API, which empowers the clients to associate with the framework utilizing straightforward, human language.

NLP based IoT Systems

Applying NLP in IoT frameworks (in various applications) can aid in tackling various issues. For instance, while driving, you notice a light on the vehicle dashboard, rather than pausing and reading the manual, the driver can inquire as to whether they need to stop promptly to get the light checked. Another example is the support specialist who is utilizing his hands and instruments to deal with a resource, and yet needs to interact with the gadget or an upkeep framework.

NLP capacities can assist with working on various cycles. Additionally, by utilizing this profound degree of information handling, and implementing it progressively on data assembled from engineers, field specialists and clients, IoT can assist organizations with making imaginative items and administrations [5].

In an API economy, designers are open using the API to begin prototyping with it. A typical model is to utilize the API for 30 minutes for more than 30 days, test it and subsequently choose how to manage it. For designers and specialists, Natural Language Processing (NLP) cases offer admittance to a rich arrangement of APIs for upgrading and further developing a consumers’s experience encompassing their IoT applications and gadgets.

At the point when an IoT system is characterized utilizing Natural Language Processing and voice perspectives, simply invocating a solitary API is not sufficient. Generally, an engineer needs to create a few APIs, characterize an API stream, characterizing the learning part of it and the input circle from the learning viewpoint, as well as preparing a dataset both at first and modifying later on.


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