A Survey of the Usages of Deep Learning in Natural Language Processing <s> Natural Language Processing <s> Abstract : Contents: Natural Language Processing Overview; Mechanical Translation, Grammars--Formal Grammars; Transformational Grammars; Systemic Grammar; Case Grammar; Parsing--Overview of Parsing Techniques; Augmented Transition Nets; The General Syntactic Processor; Text Generation; Natural Language Processing Systems--Early Natural Language Systems; Wilk's Mechanical Translation System; LUNAR, SHRDLU, MARGIE, SAM and PAM, LIFER. <s> BIB001 </s> A Survey of the Usages of Deep Learning in Natural Language Processing <s> Natural Language Processing <s> This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion <s> BIB002
The field of natural language processing, also known as computational linguistics, is an area of inquiry that has been influenced by a number of other fields such as linguistics, psychology, philosophy, cognitive science, probability and statistics, and machine learning. It involves the engineering of computational models and processes to solve practical problems in understanding human languages. These solutions are then used to build useful software that can be embedded in various settings. Work in NLP can be divided into two broad sub-areas: core areas and applications, although it is sometimes difficult to distinguish clearly to which areas some issues belong. The core areas address fundamental problems such as language modeling, which underscores quantifying associations among naturally occurring words; morphological processing, or dealing with segmentation of meaningful components of words and identifying the true parts of speech of words as used; syntactic processing or parsing, which builds sentence diagrams as possible precursors to semantic processing; and semantic processing itself, which attempts to distill meaning of words, phrases, and higher level components in a piece of text. The application areas involve topics such as extraction of useful information (e.g. named entities and relations) from documents, translation of text between and among languages, summarization of written works, automatic answering of questions by inferring the most probable answers, classification and clustering of documents in corpora, and image and video captioning. Often one needs to handle one or more of the core issues successfully and apply those ideas and procedures in order to solve practical problems. Early attempts at NLP were usually rule based, where rules were hand crafted using knowledge derived from various areas. Sometimes the rules were ad-hoc in that they were made up to solve specific problems expediently. A number of formalisms were developed to describe the syntax of natural languages, with a view toward the facilitation of parsing and semantic processing. Syntactic rules were usually paired with logic-based semantic rules to obtain semantic representations of sentences that were then used for solving practical problems. Early approaches to NLP were described in depth in widely used textbooks of the time BIB001  . single hidden layer (or no hidden layers) are said to be shallow. Feedforward networks with at least one hidden layer are often referred to as multilayer perceptrons (MLPs) BIB002 , although some definitions are more restrictive. Fig. 1 shows a few general ANN architectures.