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Let non-technical understand NLP
This article is NLP The topic, we have summarized some basic content about NLP, I believe you can understand the following questions after reading this PDF:
- What is NLP, why do you want to study NLP?
- What is the purpose of researching and applying NLP?
- What methods will NLP use in practical applications, and what are the steps?
Because it is aimed at non-technical people, there is no code or a lot of technical concepts involved in the content. You don't need a technical foundation to understand it.
Content structure in PDF
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Natural Language Processing-NLP
NLP is the bridge between humans and machines! This section will explain the importance of NLP, study the purpose of NLP, the application direction of NLP, the general methods and processes of NLP, and let everyone have a macro understanding of NLP.
Natural language understanding – NLU
Natural language understanding means that the machine is like a human being, and has the ability to understand the language of a normal person. Because natural language has many difficulties in understanding (detailed below), NLU It is still far from being human.
Natural language generation – NLG
NLG is designed to bridge the gap between humans and machines and convert non-verbal data into human-readable language formats such as articles and reports.
Word segmentation is an important step in natural language understanding – NLP. Word segmentation is the decomposition of long texts such as sentences, paragraphs, and articles into data structures in units of words, which facilitates subsequent processing and analysis.
Stem extraction and morphological restoration are important links in English corpus preprocessing. Although their purpose is the same, there are still some differences between the two. This article will introduce their concepts, similarities and differences, implementation algorithms and so on.
Part of speech tagging – Part of speech
Based on the understanding of the learning process and the understanding of related materials, this article provides a relatively comprehensive introduction to the part-of-speech tagging of natural language basic technology, including definitions, current difficulties and common methods, and also recommends a large wave of Python combat tools, including The usage of the tool.
Named entity recognition – Named-entity recognition | NER
Named Entity Recognition (NER), also known as "name identification", refers to the identification of entities with specific meaning in the text, including person names, place names, institution names, proper nouns, and so on. To put it simply, it is to identify the boundaries and categories of the entities in the natural text.
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