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Natural Language Processing is a field that covers computer understanding and manipulation of human language, and it’s ripe with possibilities for newsgathering,” Anthony Pesce said in Natural Language Processing in the kitchen. “You usually hear about it in the context of analyzing large pools of legislation or other document sets, attempting to discover patterns or root out corruption.”NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.You may think of it as the embedding doing the job supposed to be done by first few layers, so they can be skipped. Those intuitions proved correct in experiments on various tasks. 1D CNNs were much lighter and more accurate than RNNs and could be trained even an order of magnitude faster due to an easier parallelization.
One of the main challenges in language analysis is the method of transforming text into numerical input, which makes modelling feasible. It is not a problem in computer vision tasks due to the fact that in an image, each pixel is represented by three numbers depicting the saturations of three base colors. So for many years, researchers tried numerous algorithms for finding so called embeddings, which refer, in general, to representing text as vectors. Natural language processing is an interesting and difficult domain in which to develop and evaluate representation and reasoning theories. All of the problems of AI arise in this domain; solving "the natural language problem" is as difficult as solving "the AI problem" because any domain can be expressed in natural language. The field of computational linguistics has a wealth of techniques and knowledge. In this book, we can only give an overview.The NLP field saw its first major jump in improvement in the form of a semantically rich representation of words, an accomplishment enabled by the application of neural networks.. Prior to this, the most common representation was a so-called one-hot encoding, where each word is transformed into a unique binary vector with only one non-zero entry. This approach suffered greatly from sparsity, and didn’t take into account the meaning of particular words at all.
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