NLP Project 2 - Using ount Vector, TF-IDF Vector, Co-occurrence Matrix for Frequency based embeddings and made Word2Vec model using Continuous Bag of Words (CBOW) and Skip-Gram (SG) for Prediction based Embeddings
Scripts to convert correlation and p-value matrices to edge list and network
This repository contains the data used for and generated during our research for the article "Neo-Assyrian Imperial Religion Counts: A Quantitative Approach to the Affiliations of Kings and Queens with Their Gods and Goddesses." The creation of this data has been funded by the Academy of Finland (decision numbers 298647, 312051, and 330727).
🔍 Analyze PDF files effectively with this Python tool, testing compatibility across libraries to guide optimal PDF processing solutions.
Data Analytics pipeline using Apache Spark | Build multi-class classification models | Test the model using test data and compute accuracy of each method
Tool for extracting topics, keywords and their collocates from a Dutch corpus. Includes and extends the functionality of the Keyword Generator.
A fast implementation of GloVe, with optional retrofitting
Code and data for extracting co-occurrence networks from Shakespeare's plays
Co-occurrence Based Texture Synthesis
Add a description, image, and links to the co-occurence topic page so that developers can more easily learn about it.
To associate your repository with the co-occurence topic, visit your repo's landing page and select "manage topics."