Natural language processing (NLP) is a subset of Artificial Intelligence draws from a wide range of disciplines such as computer science and computational linguistics, among others. It helps computers to communicate with humans in their own language and scales other language-related tasks. Today, NLP has advanced and evolved and tend to be guided by deep learning methods based on neural networks. Such methods are aimed at emulating the function of the neurons in a human brain to ensure better performance.
NLP is already benefiting industries that function on legacy systems and paper-based works, particularly in the financial services sector. Using the technology, financial institutions might analyze a credit applicant’s risk, weigh out sentiment on their brand across the internet, and automate overall processes. NLP has come a long way from machine translation, uncovering previously invisible patterns in datasets, automating certain tasks, and freeing up people to perform higher-value, more creative work.
NLP currently is also on the rise in the face of chatbots, formulating applicable responses to customer questions by assessing the language typed into the text fields. Chatbots not only respond incoming queries from customers but also enable customers to access new information or be rerouted to germane pages abruptly. This provides a value proposition to both businesses and customers. According to an Oracle survey, 80 percent organizations already used or planned to use chatbots by 2020 for consumer-facing products.
Natural language processing techniques can be utilized to spot bogus news and can generate them in the first place. The technology uses algorithms to transform diverse, unstructured, impulsive communications into something a computer can interpret and act upon.
While every nonprofit organization is challenged to address significant societal needs and make transformational change, they spend considerably less than for-profit organizations on the same services. Integrating advanced technology could be transformative and cost-effective. As such, artificial intelligence involves its three key branches, NLP, computer vision, and speech recognition, where humans used to perform better than machines. With improved and cheaper computing power, in conjunction with advances in algorithms, it is now possible for computers to perform tasks that human ears, eyes, and brains used to do. AI has already proven quicker and more accurate, playing a vital role in enhancing transparency and access to public services, above all in terms of quality healthcare for all. NLP is also increasingly effective in helping to identify fraud in insurance systems.
NLP that provides input to computers, and when computers take these inputs and decipher by themselves what decisions to make, that process called machine learning. Machine learning algorithms enable computers to learn more and more over time as they process voluminous data and make effective decisions. Nonprofit organizations have massive datasets that they can use to develop statistical models that help them optimize fundraising. By making use of machine learning that deliver effective data insights assist nonprofits to identify and segregate donors based on various factors which can drive their marketing and fundraising efforts more effectively.