Applications Of Natural Language Processing (NLP)
These days we hear a lot about Chatbots. They are the answer to user dissatisfaction when it comes to customer care call support. They offer modern-day virtual assistants to customer’s simple problems. Discharge low-priority, high turnover tasks that do not need any skill. Intelligent Chatbots will provide consumers with customized help soon. If you have tried online shopping or interacted with a chatbox on a website. You interacted with a chatbot rather than a human being. These AI customer service gurus are in fact algorithms. They use natural language processing to understand your query. Respond in an appropriate, automatic, and in real-time to your questions.
The technology has grown to such an extent through the performance of sentiment analysis. These chatbots can respond in a manner that addresses human emotions. In other words, if you’re an angry customer trying to get your frustration out on an online shopping site’s chatbox. You will most likely connect with a bot hoping to first calm you down. Then address your concerns; they understand your anger!
The concept behind MT is to build computer algorithms for automatic translation. Without any human intervention or need. Google Translate is the best-known tool yet. Google translate is base on even an NLP field called statistical machine translation (SMT). As simple as it may seem, it is not a word-to-word replacement. It collects as much text as it can which seems to have a similar meaning between two languages. Then it analyzes the data to find the likelihood of that word used in the same meaning in the other language. And this is analogous to us humans. We begin to divide semantic meaning into words when we are young, and we interpret and extrapolate these semantic values with given word combinations.
Even though it finds its larger base in catering to direct human interaction. Machine learning has also found its way into business-to-business interactions. In the field of finance, for example, organizations have popped up with ideas to use machine translation when interacting with financial documents. Such as company annual reports. Also, key investor information documents, portfolios, etc. By using machine translation, these organizations hope to instill a system that can be available globally. Ensure a rather smooth and universal reporting experience.
Marketing agents also use NLP to find people with potential or clear intention to make a sale. Internet behavior, using social networking sites. Search engine requests provide a lot of useful unstructured customer data. Selling the correct ad for internet users helps Google to take full advantage of its sales. Market intelligence at its heart uses many information sources to build a broad picture of the existing market, consumers. More on, challenges, competition, and growth potential for new products and services. The raw data sources for this analysis include sales logs, surveys, social media, and many more.
Moreover, the use of NLP in marketing has helped deal with the issue of spam through the incorporation of spam filters. It helps marketers understand how to communicate with their customers without falling into the same category as spammers & scammers hoping to ruin someone’s day. By using NLP to understand the process and conduct further research. The marketing industry has established a set of practices aimed at improving customer experience and interaction. Which in turn helps build stronger relationships.
Voice recognition technology has been around for over 50 years? Scientists have been studying this topic for half a century. NLP has made it possible to achieve remarkable success in the last few years. We now have a whole range of speech recognition software programs that allow us to decipher the voice of humans. It’s used in mobile phones, home automation, hands-free computing, virtual support, video games, and so on. This technology is being used to drop and better other input forms such as texting, clicking, or other ways of selecting text. Currently, voice recognition is a hot topic that is part of a great number of products. A related technology is in use for text-to-speech and speech-to-text programs. They have widespread usage and use cases.
Ever heard of big data? The all-encompassing term is used to describe the field of research dedicated to extracting and analyzing data from enormous data sets. Speech recognition is one of the key facets of big data. Due to its ability to understand human language and sort it according to the researcher’s requirements. With the ability to notice trends, develop patterns and understand basic human interaction, NLP has enabled the collection of data that had before been near-impossible to access.
The Future Of Natural Language Processing
There is a statistical nature of the system behind the NLP concept. Moving towards this idea is the prospect of switching from Natural Language Processing (NLP) to Natural Language Understanding (NLU). Where users can see and feel a human emotional connection with the devices. The information technology industry has taken its leap of faith over the last decade and gone deep into the different aspects of the representation of natural languages. We hope that this technology will help us get to the fully automated world of our dreams faster and easier.