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Current approaches to natural language processing are based on deep learning, a type of AI that examines and uses patterns in data to improve a program’s understanding. The field of natural language processing has seen multiple paradigm shifts over decades, from symbolic AI to statistical methods to deep learning. We review this shift through the lens of natural language understanding , a branch of NLP that deals with “meaning”. It is a model that tries to predict words given the context of a few words before and a few words after the target word. This is distinct from language modeling, since CBOW is not sequential and does not have to be probabilistic. Typically, CBOW is used to quickly train word embeddings, and these embeddings are used to initialize the embeddings of some more complicated model. Lexical semantics plays an important role in semantic analysis, allowing machines to understand relationships between lexical items like words, phrasal verbs, etc. Syntactic analysis and semantic analysis are the two primary techniques that lead to the understanding of natural language. Bonus Materials: Question-Answering The machine interprets the important elements of the human language sentence, which correspond to specific features in a data set, and returns an answer. This involves using natural language processing algorithms to analyze unstructured data and automatically produce content based on that data. One example of this is in language models such as GPT3, which are able to analyze an unstructured text and then generate believable articles based on the text. The first part of semantic analysis, studying the meaning of individual words is called lexical semantics. It includes words, sub-words,...
