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7 INTEL 2021 Understanding Natural Language Processing Our ability to evaluate the relationship between sentences is essential for tackling a variety of natural language challenges, such as text summarization, information extraction, and machine translation. This challenge is formalized as the natural language inference task of Recognizing Textual Entailment (RTE), which involves classifying the relationship between two sentences as one of entailment, contradiction, or neutrality. For instance, the premise "Garfield is a cat", naturally entails the statement "Garfield has paws", contradicts the statement "Garfield is a German Shepherd", and is neutral to the statement "Garfield enjoys sleeping". Natural language processing is the ability of a computer program to understand human language as it is spoken. NLP is a component of artificial intelligence that deals with the interactions between computers and human languages in regard to processing and analyzing large amounts of natural language data. Natural language processing can perform several different tasks by processing the natural data through different efficient means. These tasks could include: • Answering questions about anything (what Siri, Alexa, and Cortana can do). • Sentiment analysis (determining whether the attitude is positive, negative, or neutral ) • Image to text mappings (creating captions using an input image) • Machine translation (translating text to different languages) • Speech recognition • Part of Speech (POS) tagging • Entity identification The traditional approach to NLP involved a lot of domain knowledge of linguistics itself. Deep learning, at its most basic level, is all about representation learning. With convolutional neural networks (CNN), the composition of different filters is used to classify objects. Taking a similar approach, this article creates representations of words through large datasets. Conversational AI: Natural Language Processing Features • Natural Language Processing (NLP) • Text Mining (TM) • Computational Linguistics (CL) • Machine Learning on Text Data (ML on Text) • Deep Learning approaches for Text Data (DL on Text) • Natural Language Understanding (NLU) • Natural Language Generation (NLG) Conversational AI, as shown above, has seen several amazing advances in recent years, with significant improvements in automatic speech recognition (ASR), text to speech (TTS), and intent recognition, as well as the significant growth of voice assistant devices like the Amazon Echo and Google Home. Using deep-learning techniques can work efficiently on NLP-related problems. This article uses back propagation and stochastic gradient descent (SGD) algorithms in the NLP models. Loss depends on each element of the training set, especially when it is compute-intensive, which, in the case of NLP problems, is true as the data set is large. As gradient descent is iterative, it has to be done through many steps which means going through the data hundreds and thousands of times. Estimate the loss by taking the average loss from a random, small data set chosen from the larger data set. Then compute the derivative for that sample and assumes that the derivative is the right direction to use the gradient descent. It might even increase the loss, not reduce it. Compensate by doing it many times, taking very small steps each time. Each step is cheaper to compute and overall will produce better performance. The SGD algorithm is at the core of deep learning. Introduction to Natural Language Processing (NLP) Architect "The traditional approach to NLP involved a lot of domain knowledge of linguistics itself." ▲