Learn more about Natural Language Processing and its benefits.
Natural Language Processing (NLP) allows machines to understand the meaning of human language. It is a branch of computer science that makes computers more intelligent and is part of the broader field of artificial intelligence. NLP is one of the most important parts of machine learning. For example, without access to text and language information, a computer won’t be able to understand the meaning of a photo or a video, for instance. Also, it won’t be able to understand the text in an email message. Therefore, machine learning would be powerless or, at a minimum, limited without NLP in place. These issues are crucial reasons companies such as Apple and Google use neural networks and deep learning techniques to develop NLP systems. NLP is used in many applications, including information retrieval and extraction, AI chatbots, machine translation, speech recognition and synthesis, document analysis, recommendation systems, and more!
NLP systems are composed of several subcomponents, including parsers and statistical models. A parser is a component responsible for breaking down sentences into their parts and identifying syntactic structures. A statistical model is a component that uses statistics to analyze language structures and model the data. The following is an example of NLP in practice that shows how it is used in speech recognition:
A user asks Siri to “Tell me a joke.” Siri launches the speech recognition component to search for the words in the query. The speech recognition component breaks down the sentence into words and phrases, identifies contextual relationships among the words, and calculates the probabilities of each word sequence in the context of the query.
There is much more to NLP than we can speak about on a single web page. Therefore, please book an appointment today so we can have a more in-depth look at NLP and how it can be of value to your business!
In a dynamic and digital world, intelligence is the most potent weapon: it is the base layer of enterprises’ competitive advantage and accelerated development. Machine learning (ML) is a vital technology that paves the way for a new era of intelligent organizations. Machine learning is one of the most important fields of research and development in artificial intelligence (AI). Furthermore, machine learning is a method of training machines to identify trends in large amounts of data and make accurate predictions. Leveraging big datasets helps machine learning algorithms automate data analysis and decision-making. This process allows enterprises to learn from data, find relationships, and improve their predictions for market opportunities and risk. ML algorithms are the basis for developing many applications, from scanning emails for spam to self-driving cars.
Machine learning is the science of getting computers to act without being explicitly programmed, to put it simply. Although ML is a broad field, the basic idea is pretty easy to grasp: feeding an algorithm with lots of examples, which are inherently ambiguous and noisy, allows the algorithm to “learn” which examples are best for the task at hand. ML is crucial in many fields like computer vision, natural language processing, speech recognition, et cetera. One of the most active areas of Machine Learning is Deep Learning. Deep Learning is a subset of ML and a class of ML algorithms that attempt to simulate the behavior of the human brain to process information. Thus, through machine learning, applications are in a continuous process of improvement. Combining Azure Cognitive Search, for instance, with machine learning capabilities makes your search platform even more robust as more data is loaded.
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