Home Backend Development Python Tutorial Python for NLP: How to extract and analyze footnotes and endnotes from PDF files?

Python for NLP: How to extract and analyze footnotes and endnotes from PDF files?

Sep 28, 2023 am 11:45 AM
nlp footnote Extract: pdf endnote Analysis: python

Python for NLP:如何从PDF文件中提取并分析脚注和尾注?

Python for NLP: How to extract and analyze footnotes and endnotes from PDF files

Introduction:
Natural language processing (NLP) is a combination of computer science and artificial intelligence An important research direction in the field of intelligence. As a common document format, PDF files are often encountered in practical applications. This article describes how to use Python to extract and analyze footnotes and endnotes from PDF files to provide more comprehensive text information for NLP tasks. The article will be introduced with specific code examples.

1. Install and import related libraries
To implement the function of extracting footnotes and endnotes from PDF files, we need to install and import some related Python libraries. The details are as follows:

pip install PyPDF2
pip install pdfminer.six
pip install nltk
Copy after login

Import the required libraries:

import PyPDF2
from pdfminer.high_level import extract_text
import nltk
nltk.download('punkt')
Copy after login

2. Extract PDF text
First, we need to extract plain text from the PDF file for subsequent processing. This can be achieved using the PyPDF2 library or the pdfminer.six library. The following is a sample code using these two libraries:

# 使用PyPDF2库提取文本
def extract_text_pypdf2(file_path):
    pdf_file = open(file_path, 'rb')
    pdf_reader = PyPDF2.PdfFileReader(pdf_file)
    num_pages = pdf_reader.numPages
    text = ""
    for page in range(num_pages):
        page_obj = pdf_reader.getPage(page)
        text += page_obj.extractText()
    return text

# 使用pdfminer.six库提取文本
def extract_text_pdfminer(file_path):
    return extract_text(file_path)
Copy after login

3. Extract footnotes and endnotes
Generally speaking, footnotes and endnotes are added in paper books to supplement or explain the main Text content. In PDF files, footnotes and endnotes usually appear in different forms, such as at the bottom or side of the page. To extract this additional information, we need to parse the structure and style of the PDF document.

In the actual example, we assume that the footnote is at the bottom of the page. Just analyze the plain text and find the content at the bottom of the text.

def extract_footnotes(text):
    paragraphs = text.split('

')
    footnotes = ""
    for paragraph in paragraphs:
        tokens = nltk.sent_tokenize(paragraph)
        for token in tokens:
            if token.endswith(('1', '2', '3', '4', '5', '6', '7', '8', '9')):
                footnotes += token + "
"
    return footnotes

def extract_endnotes(text):
    paragraphs = text.split('

')
    endnotes = ""
    for paragraph in paragraphs:
        tokens = nltk.sent_tokenize(paragraph)
        for token in tokens:
            if token.endswith(('i', 'ii', 'iii', 'iv', 'v', 'vi', 'vii', 'viii', 'ix')):
                endnotes += token + "
"
    return endnotes
Copy after login

4. Example Demonstration
I choose a PDF book with footnotes and endnotes as an example to demonstrate how to use the above method to extract and analyze footnotes and endnotes. Below is a complete sample code:

def main(file_path):
    text = extract_text_pdfminer(file_path)
    footnotes = extract_footnotes(text)
    endnotes = extract_endnotes(text)
    print("脚注:")
    print(footnotes)
    print("尾注:")
    print(endnotes)

if __name__ == "__main__":
    file_path = "example.pdf"
    main(file_path)
Copy after login

In the above example, we first extract plain text from the PDF file through the extract_text_pdfminer function. Then, extract footnotes and endnotes through the extract_footnotes and extract_endnotes functions. Finally, we print out the extracted footnotes and endnotes.

Conclusion:
This article introduces how to use Python to extract footnotes and endnotes from PDF files and provides corresponding code examples. Through these methods, we can understand the text content more comprehensively and provide more useful information for NLP tasks. I hope this article will be helpful to you when processing PDF files!

The above is the detailed content of Python for NLP: How to extract and analyze footnotes and endnotes from PDF files?. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. Best Graphic Settings
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. How to Fix Audio if You Can't Hear Anyone
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
WWE 2K25: How To Unlock Everything In MyRise
1 months ago By 尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

How to use Python for NLP to translate text in PDF files? How to use Python for NLP to translate text in PDF files? Sep 28, 2023 pm 01:13 PM

How to use PythonforNLP to translate text in PDF files? As globalization deepens, the need for cross-language translation is also increasing. As a common document form, PDF files may contain a large amount of text information. If we want to translate the text content in the PDF file, we can use Python's natural language processing (NLP) technology to achieve it. This article will introduce a method of using Python for NLP for PDF text translation, and

How to use Python for NLP to process tabular data in PDF files? How to use Python for NLP to process tabular data in PDF files? Sep 27, 2023 pm 03:04 PM

How to use Python for NLP to process tabular data in PDF files? Abstract: Natural Language Processing (NLP) is an important field involving computer science and artificial intelligence, and processing tabular data in PDF files is a common task in NLP. This article will introduce how to use Python and some commonly used libraries to process tabular data in PDF files, including extracting tabular data, data preprocessing and conversion

Python for NLP: How to handle PDF files containing multiple chapters? Python for NLP: How to handle PDF files containing multiple chapters? Sep 27, 2023 pm 08:55 PM

PythonforNLP: How to handle PDF files containing multiple chapters? In natural language processing (NLP) tasks, we often need to process PDF files containing multiple chapters. These documents are often academic papers, novels, technical manuals, etc., and each chapter has its own specific format and content. This article will introduce how to use Python to process such PDF files and provide specific code examples. First, we need to install some Python libraries to help us process PDF files. The most commonly used ones are

An article on time series forecasting under the wave of large-scale models An article on time series forecasting under the wave of large-scale models Nov 06, 2023 am 08:13 AM

Today I will talk to you about the application of large models in time series forecasting. With the development of large models in the field of NLP, more and more work attempts to apply large models to the field of time series prediction. This article introduces the main methods of applying large models to time series forecasting, and summarizes some recent related work to help everyone understand the research methods of time series forecasting in the era of large models. 1. Large model time series forecasting methods. In the past three months, a lot of large model time series forecasting work has emerged, which can basically be divided into two types. Rewritten content: One approach is to directly use large-scale models of NLP for time series forecasting. In this method, large-scale NLP models such as GPT and Llama are used for time series prediction. The key lies in how to

The difference between footnotes and endnotes The difference between footnotes and endnotes Mar 07, 2024 pm 03:26 PM

The difference between footnotes and endnotes: 1. Definition and location; 2. Content and format; 3. Usage occasions and text length; 4. Usage effects and emphasis. Detailed introduction: 1. Definition and location. Footnotes are usually located at the bottom of the page. They are a way of explaining or explaining additional information about certain content in the document. Endnotes are located at the end of the entire document and are a summarized reference. List; 2. Content and format. The content of footnotes is usually brief, including brief notes or source citations, etc.

TabTransformer converter improves multi-layer perceptron performance in-depth analysis TabTransformer converter improves multi-layer perceptron performance in-depth analysis Apr 17, 2023 pm 03:25 PM

Today, Transformers are key modules in most advanced natural language processing (NLP) and computer vision (CV) architectures. However, the field of tabular data is still dominated by gradient boosted decision tree (GBDT) algorithms. So, there were attempts to bridge this gap. Among them, the first converter-based tabular data modeling paper is the paper "TabTransformer: Tabular Data Modeling Using Context Embedding" published by Huang et al. in 2020. This article aims to provide a basic presentation of the content of the paper, while also delving into the implementation details of the TabTransformer model and showing you how to specifically use Ta for our own data.

How to convert PDF text to editable format using Python for NLP? How to convert PDF text to editable format using Python for NLP? Sep 28, 2023 am 10:52 AM

How to convert PDF text to editable format using PythonforNLP? In the process of natural language processing (NLP), we often encounter the need to extract information from PDF text. However, since PDF text is usually not editable, this brings certain problems to NLP processing. Fortunately, using some powerful libraries of Python, we can easily convert PDF text into editable format and process it further. This article will introduce how to use Python

Python for NLP: How to extract and analyze footnotes and endnotes from PDF files? Python for NLP: How to extract and analyze footnotes and endnotes from PDF files? Sep 28, 2023 am 11:45 AM

PythonforNLP: How to extract and analyze footnotes and endnotes from PDF files Introduction: Natural language processing (NLP) is an important research direction in the fields of computer science and artificial intelligence. As a common document format, PDF files are often encountered in practical applications. This article describes how to use Python to extract and analyze footnotes and endnotes from PDF files to provide more comprehensive text information for NLP tasks. The article will be introduced with specific code examples. 1. Install and import related libraries to achieve from

See all articles