Overview of Natural Language Processing (NLP)

Below is a high-level overview of Natural Language Processing (NLP).

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Natural Language Processing (NLP) Machine Learning The processing of any natural language in order to understand both its grammatical syntax and semantics Natural Language Any language that has evolved naturally in humans (as opposed to constructed and formal languages for computers or logic) Categories of NLP Syntax-related Parsing grammar Grammar induction Word and sentence breaking / segmentation / boundary disambiguation Part-of-speech tagging (e.g. as nouns, verbs etc) Terminology extraction (finding relevant terms in a corpus) Semantic-related Language translation Lexical semantics & word-sense disambiguation (identifying the correct meaning of the words in the given context) Named entity recognition (identification and understanding of proper names/people/places) Generation of natural language Optical Character Recognition (OCR) Question answering Relationship extraction (identifying and understanding relationships among named entities) Sentiment analysis / opinion mining (identifying, extracting and quantifying emotional states, polarised opinions and subjective information). Text-based or multi-modal with audio and visual data. Topic segmentation / classification (identifying the topic of chunks of the corpus) Discourse-related Summary generation (producing a summary of a corpus) Co-reference resolution (determining which words refer to the same thing - e.g. when there are two or more nouns mentioned, which does a pronoun refer to?) Discourse analysis (e.g. identifying connected parts of text or speech acts - such as conversation questions and answers, statements, assertiongs) Speech-related Speech-to-Text / speech recognition Text-to-Speech