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Topic Models

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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.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/dangitstam/topic-rnn

TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency

https://github.com/derekgreene/topic-stability

How Many Topics? Stability Analysis for Topic Models

https://github.com/dfm/arxiv-analysis

Stochastic Variational Inference

https://github.com/dmlc/experimental-lda

LightLDA: Big Topic Models on Modest Compute Clusters

https://github.com/folivetti/HBLCoClust

Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec

https://github.com/fumiyo0607/ETM

Topic Modeling in Embedding Spaces

https://github.com/gonsoomoon-ml/topic-modeling

Coherence-Aware Neural Topic Modeling

https://github.com/hjzzang/ETM

Topic Modeling in Embedding Spaces

https://github.com/is0383kk/Dirichlet_VAE

Autoencoding Variational Inference For Topic Models

https://github.com/jainshobhit/Variational-Autoencoder

Neural Variational Inference for Text Processing

https://github.com/johntailor/bertsenclu

Topic Modeling based on Keywords and Context

https://github.com/kmpoon/hlta

Latent Tree Models for Hierarchical Topic Detection

https://github.com/labixiaoK/lda

Supervised Topic Models

https://github.com/lffloyd/embedded-topic-model

Topic Modeling in Embedding Spaces

https://github.com/lfmatosm/embedded-topic-model

Topic Modeling in Embedding Spaces

https://github.com/lppier/Topic_Modelling_Top2Vec_BERTopic

Top2Vec: Distributed Representations of Topics

https://github.com/marshsh/SMH-extended

Topic Discovery in Massive Text Corpora Based on Min-Hashing

https://github.com/maximilianahrens/scholar4regression

Neural Models for Documents with Metadata

https://github.com/memray/llm_phrase_semantics

Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration

https://github.com/migrationsKB/MGKB

Topic Modeling in Embedding Spaces

https://github.com/moontae/jsmf-raw

Learning Topic Models - Going beyond SVD

https://github.com/oleksost/Stochastic_VI_LDA

Stochastic Variational Inference

https://github.com/qijindou/CRAB

LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models

https://github.com/rpryzant/causal-bert-pytorch

Adapting Text Embeddings for Causal Inference

https://github.com/sb1992/NETL-Automatic-Topic-Labelling-

Automatic Labelling of Topics with Neural Embeddings

https://github.com/sc782/pyJSMF-RAW

Learning Topic Models - Going beyond SVD

https://github.com/shining-spring/nvlda

Neural Variational Inference for Text Processing

https://github.com/skesiraju/BaySMM

Learning document embeddings along with their uncertainties

https://github.com/sophieburkhardt/dirichlet-vae-topic-models

Dirichlet Variational Autoencoder

https://github.com/tshi04/AspDecSSCL

A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection

https://github.com/vlukiyanov/pt-avitm

Autoencoding Variational Inference For Topic Models

https://github.com/yjxiao/ProdLDA

Autoencoding Variational Inference For Topic Models

https://github.com/ysmiao/nvdm

Neural Variational Inference for Text Processing

https://github.com/yukisea/ETM

Topic Modeling in Embedding Spaces

https://github.com/zhehengluok/gctm

Contrastive Learning for Neural Topic Model

https://github.com/zihao12/Top_Experiments

A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics

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