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Non-technical picture to understand NLP

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:

  1. What is NLP, why do you want to study NLP?
  2. What is the purpose of researching and applying NLP?
  3. 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

NLP concept structure

The above picture is the main content involved in the PDF. Let me explain it to you:

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.

Learn more:"A text to understand natural language processing - NLP (4 typical applications + 5 difficult points + 6 implementation steps)"

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.

Learn more:"A text to understand natural language understanding - NLU (basic concept + practical application + 3 implementation)"

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.

Learn more:"A text to understand natural language generation – NLG (6 implementation steps + 3 typical applications)"

Participle- Tokenization

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.

Learn more:"A text to understand the word segmentation in the NLP - Tokenization (Chinese and English difference + 3 difficult + 3 typical method)"

Stem extraction – Stemming | Word form restoration – Lemmatisation

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.

Learn more:"A text to understand stem extraction - Stemming and morphological restoration - Lemmatisation (concepts, similarities and differences, algorithms)"

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.

Learn more:"I read the part of speech tagging (basic concept + 4 method + 7 kind of 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.

Learn more:"One article to understand named entity recognition-NER (development history + 4 types of methods + data set + tool recommendation)"

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