Papers with Code · CC BY-SA 4.0

Topic Models

Classification › Text Classification › Topic Models 23 평가 기록 9 datasets

A topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for the discovery of hidden semantic structures in a text body.

논문

A Topic Coverage Approach to Evaluation of Topic Models

2012.06274 2020-12-11 이 페이지의 평가 5건

Spearman Correlation: 0.95

vONTSS: vMF based semi-supervised neural topic modeling with optimal transport

2307.01226 2023-07-03 이 페이지의 평가 3건

C_v: 0.49NPMI: 0.054

Autoencoding Variational Inference For Topic Models

1703.01488 2017-03-04 이 페이지의 평가 2건

C_v: 0.32NPMI: -0.22

Discovering Discrete Latent Topics with Neural Variational Inference

1706.00359 2017-06-01 이 페이지의 평가 2건

C_v: 0.41NPMI: 0.03

Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding

2007.09536 2020-07-18 이 페이지의 평가 2건

Topic coherence@5: 0.0074MACC: 83.24

Neural Topic Model via Optimal Transport

2008.13537 2020-08-12 이 페이지의 평가 2건

C_v: 0.37NPMI: -0.04

Spherical Latent Spaces for Stable Variational Autoencoders

1808.10805 2018-08-31 이 페이지의 평가 2건

C_v: 0.44NPMI: 0.028

Topic Modeling in Embedding Spaces

1907.04907 2019-07-08 이 페이지의 평가 2건

C_v: 0.41NPMI: 0.02

Learning document embeddings along with their uncertainties

1908.07599 2019-08-20 이 페이지의 평가 1건

Test perplexity: 515

Neural Variational Inference for Text Processing

1511.06038 2015-11-19 이 페이지의 평가 1건

Test perplexity: 836

TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts

1902.06034 2019-02-16 이 페이지의 평가 1건

Topic Coherence@50: 0.097