NLP with Python & SpaCy PDF 📄

Download this code from https://codegive.com
Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and humans using natural language. In this tutorial, we will explore NLP using Python and the spaCy library. SpaCy is a powerful and efficient library for NLP tasks, providing pre-trained models for various languages and a user-friendly API.
Before you start, make sure you have Python installed on your system. You can install spaCy using the following command:
Additionally, we'll need to download a spaCy model for English. You can download the default English model using the following command:
Now, let's dive into various aspects of NLP using spaCy.
Tokenization is the process of breaking down text into smaller units, typically words or phrases. SpaCy makes tokenization straightforward:
SpaCy can also perform part-of-speech tagging, assigning grammatical categories to each token:
Named Entity Recognition is the task of identifying entities (e.g., names, organizations, locations) in text:
Dependency parsing involves analyzing the grammatical structure of a sentence by identifying the relationships between words:
Now, let's extend our tutorial to process PDF documents using spaCy. To accomplish this, we'll use the PyMuPDF library to extract text from PDFs and then apply spaCy for NLP tasks.
First, install the required library:
This example demonstrates how to extract text from a PDF and apply basic NLP tasks using spaCy.
In this tutorial, we covered fundamental NLP tasks using spaCy in Python, including tokenization, part-of-speech tagging, named entity recognition, and dependency parsing. We also extended our exploration to process PDF documents, combining spaCy with the PyMuPDF library for a more comprehensive NLP workflow.
Explore spaCy's documentation (https://spacy.io/) for more advanced features and customization options. Happy coding!
ChatGPT