This repo stores the codes for topic modeling on palliative care journals.

Last update: Dec 20, 2022

Overview

This repo stores the codes for topic modeling on palliative care journals.

Data Preparation

You first need to download the journal papers.

bash 1_download_pdfs.sh # To download papers from the journal `jpm`
bash 1_download_pdfs_jwh.sh # To download papers from the journal `jwh`

Environment Setup

Install all the necessary python packages.

bash 2_pdf2json_prep_env.sh

How to Run the Topic Model

Run the topic modeling on the default journal jpm:

python 3_pdf2json2topics.py

Or you can also run the topic modeling on the other journal jwh:

python 3_pdf2json2topics.py -journal_name jwh

Owner

PhD in NLP & Causality. Affiliated with Max Planck Institute, Germany & ETH & UMich. Supervised by Bernhard Schoelkopf, Rada Mihalcea, and Mrinmaya Sachan.

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This repo stores the codes for topic modeling on palliative care journals.

Related tags

Overview

Data Preparation

Environment Setup

How to Run the Topic Model

Owner

Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5

Code examples for my Write Better Python Code series on YouTube.

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An easy-to-use Python module that helps you to extract the BERT embeddings for a large text dataset (Bengali/English) efficiently.

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FedNLP: A Benchmarking Framework for Federated Learning in Natural Language Processing

Code and checkpoints for training the transformer-based Table QA models introduced in the paper TAPAS: Weakly Supervised Table Parsing via Pre-training.

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DVC-NLP-Simple-usecase

Deep Learning for Natural Language Processing - Lectures 2021

A library for Multilingual Unsupervised or Supervised word Embeddings