[WWW 2022] Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations
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Updated
Feb 10, 2022 - Python
[WWW 2022] Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations
This repository contains the Domain Discovery Tool (DDT) project. DDT is an interactive system that helps users explore and better understand a domain (or topic) as it is represented on the Web.
Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds (NAACL'22)
Domain Discovery Operations API formalizes the human domain discovery process by defining a set of operations that capture the essential tasks that lead to domain discovery on the Web as we have discovered in interacting with the Subject Matter Experts (SME)s.
A repository for "The Latent Semantic Space and Corresponding Brain Regions of the Functional Neuroimaging Literature" -- http://www.biorxiv.org/content/early/2017/07/20/157826
Applying unsupervised learning using K-means clustering.
News topic discovery using LDA (Latent Dirichlet Allocation)
2º trabalho prático da disciplina de Banco de Dados 2017 - UFSJ
Automated topic discovery crawler
Add a description, image, and links to the topic-discovery topic page so that developers can more easily learn about it.
To associate your repository with the topic-discovery topic, visit your repo's landing page and select "manage topics."