Introduction to Natural Language Processing (NLP)

Introduction to Natural Language Processing (NLP)

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Recently, I encountered a Kaggle NLP competition (see my previous article for details), so I decided to organize some related content on NLP. This article mainly introduces three questions: What is NLP? What can it be used for? How is it implemented?

What Is NLP?

NLP stands for Natural Language Processing, which is a field of data science primarily used to address language issues.

If you are encountering this for the first time, you might ask, what is so important about language? In fact, language is crucial; it is a hallmark of human civilization and a carrier of information transmission.

Once language is input into a computer, we can process, organize, and analyze it, which has broad applications in the fields of science, economy, society, and culture.

What Can NLP Be Used For?

Here, we will discuss some relatively advanced applications, such as:

  • Understanding the meaning of individual characters or phrases. A word may have multiple meanings, and NLP can infer what it should mean in context.

  • Detecting the subject, predicate, and object of a sentence. Simply put, it can identify the structure of a sentence.

  • Text classification, such as identifying malicious content in comments on platforms like Douyin or Weibo.

  • Language generation. For example, given the sentence “The sky is blue,” a program might ask, “What color is the sky?”

  • Machine translation, which many people use frequently, although the quality of translations varies significantly between different tools. I recommend Google Translate.

  • Human-machine dialogue. Siri, Xiao Ai, Xiao Na, and various intelligent customer service representatives are all examples of human-machine conversations, but NLP here focuses solely on text understanding and generation.

  • Understanding meaning. For instance, given the statement: “A Tu has eight-pack abs,” and another statement: “A Tu weighs two hundred pounds,” you would need to judge the truth of the latter statement.

  • And so on…

How Is NLP Implemented?

Currently, it mainly relies on programming languages to process language text. In future articles, we will use Python along with the NLTK and Spacy libraries to provide relevant examples.

Taking NLTK as an example, when we feed a segment of text into NLTK, it can split this text into sentences and tokens. For the following text, sentences are the elements divided by periods, and tokens are the words.

Hello! This is atu speaking. Welcome to read my passage and have a good time!

The tokens here are fascinating; they not only represent individual words, but if paired with a vocabulary, you can also know the part of speech, pronunciation, and other information about these tokens.

Some people have specifically numbered tens of thousands of commonly used words, for example, apple is numbered 1, cow is numbered 2, and so on. This way, you can represent a segment of text entirely with a series of numerical codes. What are the benefits of converting to numerical codes? This is very useful in text classification, as it facilitates the statistical analysis of word frequency and, with some training using machine learning, can yield good classification results.

That’s all for today; this is just a brief overview of NLP. In future articles, I will write about current popular methods and processing techniques related to competitions, so don’t miss out!

Follow “A Tu Classmate” and let’s dive into AI together! Starting address: atu.ai

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