Topic Models

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

6 leaderboard tables shown for this task, 6 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

17 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 229 papers with code (881 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Topic Modeling in Embedding Spaces
  8 Jul 2019 12 repositories listed Syntology ran 3 of 13 samples · 10 unverified
  To this end, we develop the Embedded Topic Model (ETM), a generative model of documents that marries traditional topic models with word embeddings.
• Autoencoding Variational Inference For Topic Models
  4 Mar 2017 6 repositories listed Syntology ran 1 of 8 samples · 7 unverified · 1 pointer-only (licence)
  A promising approach to address this problem is autoencoding variational Bayes (AEVB), but it has proven diffi- cult to apply to topic models in practice.
• Neural Variational Inference for Text Processing
  19 Nov 2015 6 repositories listed Syntology ran 1 of 4 samples · 3 unverified · 1 pointer-only (licence)
  We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering.
• Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
  6 May 2016 5 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  Distributed dense word vectors have been shown to be effective at capturing token-level semantic and syntactic regularities in language, while topic models can form interpretable representations over documents.
• Adapting Text Embeddings for Causal Inference
  29 May 2019 4 repositories listed
  To address this challenge, we develop causally sufficient embeddings, low-dimensional document representations that preserve sufficient information for causal identification and allow for efficient estimation of causal…
• BERTopic: Neural topic modeling with a class-based TF-IDF procedure
  11 Mar 2022 3 repositories listed Syntology ran 1 of 8 samples · 7 unverified
  BERTopic generates coherent topics and remains competitive across a variety of benchmarks involving classical models and those that follow the more recent clustering approach of topic modeling.
• Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence
  8 Apr 2020 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data.
• Topic Discovery in Massive Text Corpora Based on Min-Hashing
  3 Jul 2018 3 repositories listed
  This paper describes an alternative approach to discover topics based on Min-Hashing, which can handle massive text corpora and large vocabularies using modest computer hardware and does not require to fix the number of…
• An Unsupervised Neural Attention Model for Aspect Extraction
  1 Jul 2017 3 repositories listed
  Unlike topic models which typically assume independently generated words, word embedding models encourage words that appear in similar contexts to be located close to each other in the embedding space.
• Neural Models for Documents with Metadata
  25 May 2017 3 repositories listed Syntology ran 6 of 6 samples · 0 unverified
  Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information.
• LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models
  13 Jun 2024 2 repositories listed
  Topic modeling has been a widely used tool for unsupervised text analysis.
• FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model
  28 May 2024 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  We further propose a novel Embedding Transport Plan (ETP) method.
• Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion
  28 May 2024 2 repositories listed
  However, existing models suffer from repetitive topic and unassociated topic issues, failing to reveal the evolution and hindering further applications.
• A Survey on Neural Topic Models: Methods, Applications, and Challenges
  27 Jan 2024 2 repositories listed
  In this paper, we present a comprehensive survey on neural topic models concerning methods, applications, and challenges.
• Effective Neural Topic Modeling with Embedding Clustering Regularization
  7 Jun 2023 2 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 2 pointer-only (licence)
  Topic models have been prevalent for decades with various applications.
• InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling
  7 Apr 2023 2 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 2 pointer-only (licence)
  Instead of the direct alignment in previous work, we propose a topic alignment with mutual information method.
• Improving Neural Topic Models with Wasserstein Knowledge Distillation
  27 Mar 2023 2 repositories listed
  Topic modeling is a dominant method for exploring document collections on the web and in digital libraries.
• Improving Contextualized Topic Models with Negative Sampling
  27 Mar 2023 2 repositories listed
  Topic modeling has emerged as a dominant method for exploring large document collections.
• Nonparametric Forest-Structured Neural Topic Modeling
  1 Oct 2022 2 repositories listed
  Neural topic models have been widely used in discovering the latent semantics from a corpus.
• Principled Analysis of Energy Discourse across Domains with Thesaurus-based Automatic Topic Labeling
  1 Dec 2021 2 repositories listed
  With the increasing impact of Natural Language Processing tools like topic models in social science research, the experimental rigor and comparability of models and datasets has come under scrutiny.
• Contrastive Learning for Neural Topic Model
  25 Oct 2021 2 repositories listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
  Recent empirical studies show that adversarial topic models (ATM) can successfully capture semantic patterns of the document by differentiating a document with another dissimilar sample.
• Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration
  13 Sep 2021 2 repositories listed Syntology ran 2 of 9 samples · 7 unverified · 2 pointer-only (licence)
  Phrase representations derived from BERT often do not exhibit complex phrasal compositionality, as the model relies instead on lexical similarity to determine semantic relatedness.
• Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence
  5 Jul 2021 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  To address the standardization gap, we systematically evaluate a dominant classical model and two state-of-the-art neural models on two commonly used datasets.
• Using Transformer based Ensemble Learning to classify Scientific Articles
  19 Feb 2021 2 repositories listed
  The first one is a RoBERTa [10] based model built over these abstracts.
• Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder
  1 Nov 2020 2 repositories listed
  Topic models have been prevailing for many years on discovering latent semantics while modeling long documents.
• Top2Vec: Distributed Representations of Topics
  19 Aug 2020 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Distributed representations of documents and words have gained popularity due to their ability to capture semantics of words and documents.
• Cross-lingual Contextualized Topic Models with Zero-shot Learning
  16 Apr 2020 2 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  They all cover the same content, but the linguistic differences make it impossible to use traditional, bag-of-word-based topic models.
• A Coefficient of Determination for Probabilistic Topic Models
  20 Nov 2019 2 repositories listed
  This research proposes a new (old) metric for evaluating goodness of fit in topic models, the coefficient of determination, or R².
• Learning document embeddings along with their uncertainties
  20 Aug 2019 2 repositories listed
  We present Bayesian subspace multinomial model (Bayesian SMM), a generative log-linear model that learns to represent documents in the form of Gaussian distributions, thereby encoding the uncertainty in its co-variance.
• Dirichlet belief networks for topic structure learning
  2 Nov 2018 2 repositories listed
  Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures.

Syntology lines on 15 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.