Automatic keyword extraction methods from individual documents.
-
Updated
Feb 21, 2024 - Jupyter Notebook
Automatic keyword extraction methods from individual documents.
This Python project shows how to build a content based recommendation system. Data is related to movies.
PDF keyword extraction using Python 3. Extract text from a PDF document and determine key phrases in a body of text by analyzing the frequency of word appearance and its co-occurance with other words in the text.
The project is a Python implementation of a Text Summarizer. It uses various natural language processing (NLP) techniques to generate a summary of a given text.
Gap filled question generator
PDF Notes + Deep Learning -> AI-Generated Slides
Keyword based searching and matching algorithm using Deep NLP
"Advertising platform ,find the relevant keywords on blog and then find ads which are relevant to them automatically"
In this project, I explore a TripAdvisor hotel review dataset with the LDA algorithm, Rapid Keyword Extraktion (RAKE)
A miniature Java Search Engine using the Rapid Automatic Keyword Extraction Framework ( RAKE ) and HashMaps
A self-contained Java15 implementation of the Rapid Automatic Keyword Extraction Framework ( RAKE ) for keyword extraction.
Keyword/entity/phrases identification & a possible approach to map to categories
A movie recommendation web-based application that recommends movies (using a content-based filtering algorithm) to a user.
Source-Recommendation-System takes an article from the user as input and outputs any relevant article from a dataset of 8.5 million articles.
👀 A very simple sentence classifier based on word similarity with NLTK and rake_nltk package
Add a description, image, and links to the rake-nltk topic page so that developers can more easily learn about it.
To associate your repository with the rake-nltk topic, visit your repo's landing page and select "manage topics."