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Topic Models
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- 평가표
- 6
- 보존된 평가 기록
- 18
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/x-tabdeveloping/turftopic
공식 코드https://github.com/xiaohao-yang/topic_model_evaluation
공식 코드LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models
https://github.com/xiaohao-yang/topic_model_generalisation
공식 코드https://github.com/yiruiliu110/ConditionalVI
공식 코드https://github.com/yuewang-cuhk/HashtagGeneration
공식 코드Microblog Hashtag Generation via Encoding Conversation Contexts
https://github.com/yueyu1030/ReGen
공식 코드ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
https://github.com/yumeng5/CatE
공식 코드Discriminative Topic Mining via Category-Name Guided Text Embedding
https://github.com/yumeng5/JoSH
공식 코드Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
https://github.com/yumeng5/topclus
공식 코드Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations
https://github.com/yuzhimanhua/seetopic
공식 코드https://github.com/zli12321/tenor
공식 코드Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis
https://zenodo.org/record/6842883
공식 코드https://github.com/AVBelyy/Rysearch
Construction and Quality Evaluation of Heterogeneous Hierarchical Topic Models
https://github.com/AhmedHlel/soen691-topk-topics
https://github.com/Ardavans/sHDP
https://github.com/Arikskigin/Lda2vec_Implementation
Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
https://github.com/BUTSpeechFIT/BaySMM
https://github.com/Fred-Chung/UBD
https://github.com/GatorSense/PMLDA
https://github.com/TommyJones/tidylda
A Coefficient of Determination for Probabilistic Topic Models
https://github.com/Vokturz/tsnmf-sparse
https://github.com/Wurmloch/TopicModeling
Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
https://github.com/Xiaohao-Yang/LLM-ITL
Neural Topic Modeling with Large Language Models in the Loop
https://github.com/YongfeiYan/Neural-Document-Modeling
https://github.com/akashgit/autoencoding_vi_for_topic_models
https://github.com/alexismailov2/FastDTM
https://github.com/amaiya/causalnlp
https://github.com/arodri202/dl-final-project
Learning the Latent "Look": Unsupervised Discovery of a Style-Coherent Embedding from Fashion Images
https://github.com/avinashok/TextCategorization
Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
https://github.com/bnosac/ETM
https://github.com/bondfeld/BNC_idioms
Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions
https://github.com/carpedm20/variational-text-tensorflow
https://github.com/cran/tidylda
A Coefficient of Determination for Probabilistic Topic Models
https://github.com/cran/topicmodels.etm
https://github.com/d2klab/tomodapi
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence
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