There are 55 repositories under topic-modeling topic.
Beautiful visualizations of how language differs among document types.
A Python toolbox for gaining geometric insights into high-dimensional data
A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021 (Bianchi et al.).
Interact, analyze and structure massive text, image, embedding, audio and video datasets
Fast vectorization, topic modeling, distances and GloVe word embeddings in R.
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx
Resources for learning about Text Mining and Natural Language Processing
An off-the-shelf tool for Chinese Keyphrase Extraction 一个快速从中文里抽取关键短语的工具,仅占35M内存 www.jionlp.com
Various Algorithms for Short Text Mining
Machine Learning Lectures at the European Space Agency (ESA) in 2018
Code for acl2017 paper "An unsupervised neural attention model for aspect extraction"
LDA topic modeling for node.js
Open Source Package for Gibbs Sampling of LDA
Palmetto is a quality measuring tool for topics
Topic modeling helpers using managed language models from Cohere. Name text clusters using large GPT models.
Text Mining and Topic Modeling Toolkit for Python with parallel processing power
Improving topic models LDA and DMM (one-topic-per-document model for short texts) with word embeddings (TACL 2015)
The official implementation of ACL 2019 paper "Topic-Aware Neural Keyphrase Generation for Social Media Language"
A Framework for Textual Entailment based Zero Shot text classification
The purpose of this project was to defeat the current Application Tracking System used by most of the organization to filter out resumes. In order to achieve this goal I had to come up with a universal score which can help the applicant understand the current status of the match. The following steps were undertaken for this project 1) Job Descriptions were collected from Glass Door Web Site using Selenium as other scrappers failed 2) PDF resume parsing using PDF Miner 3) Creating a vector representation of each Job Description - Used word2Vec to create the vector in 300-dimensional vector space with each document represented as a list of word vectors 4) Given each word its required weights to counter few Job Description specific words to be dealt with - Used TFIDF score to get the word weights. 5) Important skill related words were given higher weights and overall mean of each Job description was obtained using the product for word vector and its TFIDF scores 6) Cosine Similarity was used get the similarities of the Job Description and the Resume 7) Various Natural Language Processing Techniques were identified to suggest on the improvements in the resume that could help increase the match score