Topic Models
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Benchmarks
Most implemented
Topic Modeling in Embedding Spaces
Autoencoding Variational Inference For Topic Models
Neural Variational Inference for Text Processing
Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
Adapting Text Embeddings for Causal Inference
BERTopic: Neural topic modeling with a class-based TF-IDF procedure
Papers
TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale
Organizing unstructured feedback text into hierarchical taxonomy is a fundamental challenge in NLP, particularly in domains where feedback arrives at massive scale in varied forms such as reviews, transcripts, and survey…
Topic ModelsLabel Semantic Expansion via Label Guided Neural Topic Modeling
Topic models are widely used for content analysis, where users often analyze corpora around predefined labels rather than unordered latent topics. Existing label-aware topic models mainly follow a labels-for-topics persp…
Topic ModelsFlesch-Kincaid Readability Depends Only on the Topic Distribution in Long Texts under Topic Models
Flesch Reading Ease (FRE) and the Flesch-Kincaid Grade Level (FKGL) are widely used readability scores for English computed from the same two document statistics, yet their stability on long documents need not imply inva…
Topic ModelsDynamic Topic Modeling for Cross-Corpus Temporal Analysis
Dynamic Embedded Topic Models (D-ETM) provide an interpretable framework for modeling temporal semantic evolution, but cross-corpus comparison remains difficult because topics are often learned independently and aligned …
Topic ModelsDomain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation
Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-training, yet their topic interpretability often degrades on specialized co…
parameter-efficient fine-tuningTopic ModelsWhen Lexical Change Misleads: Rethinking Dynamic Topic Model Evaluation with Traditional and LLM-Based Metrics
Dynamic topic models capture evolving word distributions, but traditional coherence metrics may fail when vocabulary changes while semantic meaning persists. We evaluate 120 topics from CoNTM and DLDA across NYT, DBLP, a…
Semantic SimilarityTopic Models