Cute python: Getting started with the Natural language toolkit

In this installment, David introduces you to the Natural Language Toolkit (Natural Language Toolkit), a Python library that applies academic language technology to a text dataset. The program called "Text Processing" is its basic function, and more deeply is devoted to the study of the grammar of natural language and the ability of semantic analysis.

I am not well-informed, although I have written a lot about text processing (for example, a book), but for me, language processing (linguistic processing) is a relatively novel field. If there is an error in the description of the significant Natural Language Toolkit (NLTK), please understand. NLTK is an excellent tool for using Python teaching as well as practical computational linguistics. In addition, computational linguistics is closely related to artificial intelligence, language/specialized language recognition, translation and grammar checking.

What does NLTK include?

NLTK will naturally be seen as a series of layers with stack structures built on each other. For those familiar with the grammar and parsing of the human language (such as Python), it is not too difficult to understand the similar-but more esoteric-layers of the natural language model.

Terminology List

Complete set (Corpora): A collection of related text. For example, Shakespeare's works may be collectively referred to as a Corpus (corpus), while the works of several authors are called complete.

Histogram (histogram): the statistical distribution of the frequency of occurrences of different words, letters, or other entries in a data set.

Structure (syntagmatic): The study of the paragraph, that is, the continuous occurrence of letters, words or phrases in the complete statistical relationship.

Context-free syntax (Context-free grammar): The second category in the Noam Chomsky hierarchy consisting of four classes of formal syntax. See resources for a detailed description.

Although NLTK comes with many of the complete collections that have been preprocessed (usually manually) to varying degrees, each layer of the concept relies on adjacent, lower-level processing. The first is word-breaking; then the words are tagged; then the group words are parsed into grammatical elements, such as noun phrases or sentences (depending on one of several techniques, each of which has its advantages and disadvantages), and finally the final statement or other syntactic units are categorized. With these steps, NLTK allows you to generate statistics about the occurrence of different elements and to draw graphs that describe the process itself or the results of statistical totals.

In this article, you will see some relatively complete examples of low-level capabilities, and most high-level capabilities will simply be described in simple abstractions. Now let's take a detailed analysis of the first steps of text processing.