New algorithm improves topic modeling for short texts.
problem Inference of latent topics from short texts is challenging.
method Developed a stochastic divergence minimization algorithm for Biterm Topic Model (BTM).
result The new algorithm estimates latent topics more accurately and efficiently.
AOBTM adapts online topic modeling for short app reviews, revealing coherent topics over time.
problem Challenges in inferring latent topics from short, dynamic app reviews over multiple versions.
method Adaptive Online Biterm Topic Model (AOBTM) that addresses sparsity and considers statistical data from previous versions.
result AOBTM finds more coherent topics and outperforms state-of-the-art baselines.
BBM models short texts using biterms to improve coherence.
problem Challenges in analyzing short texts from social media.
method Bag of Biterms (BoB) for document representation and simple statistical models.
result BBM enhances coherence and performance over traditional models.
Proposes a new multi-layer model for topic distributions.
problem Leveraging deep structures for learning word distributions of topics.
method A multi-layer generative process on word distributions of topics, where each topic is drawn from a mixture of topics from the layer above.
result Discover interpretable topic hierarchies and improve topic models' accuracy and interpretability.
Efficiently models correlated topics with topic embeddings.
problem High computational cost and poor scaling in correlated topic modeling.
method Compact topic embeddings and efficient inference in low-dimensional space.
result Handles larger model and data scales without sacrificing performance.
Topic models are probabilistic models for discovering topical themes in collections of documents. In real world applications, these models provide us with the means of organizing what would otherwise be unstructured collections. They can help us cluster a huge collection into different topics or find a subset of the co…
Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local top…
ETM discovers interpretable topics in large vocabularies.
problem Existing topic models fail with large, heavy-tailed vocabularies.
method Generative model combining topic models and word embeddings with variational inference.
result ETM discovers interpretable topics even with large vocabularies.
A new model CDTM improves text classification by concentrating document topics.
problem Unsupervised text classification with diverse topic distributions.
method Imposes an exponential entropy penalty on document topic distribution to encourage concentration.
result More coherent topics and concentrated, sparse document-topic distributions.
Paper finds better words for topic models by reranking top words.
problem Top words in topic models are not always representative.
method Reranking words by considering marginal probability over every topic.
result Reranked top words are more representative of topics.
Predictive topic models retain only relevant terms for better prediction and topic coherence.
problem Misspecification of topic models leads to poor prediction and topic coherence.
method Uses supervisory signal to select vocabulary terms improving prediction performance.
result Prediction-focused topic models learn more coherent topics while maintaining competitive predictions.
New models discover new topics over time in topic modeling.
problem Discovering new topics over time in topic modeling.
method Nonparametric Bayesian models and Hungarian matching algorithm.
result Significantly faster than existing methods, discovering new topics in large datasets.
New metric correlates local topic quality with human judgments.
problem Evaluation of topic models focuses on global metrics, ignoring token-level assignments.
method Proposed a human evaluation task and automated metrics to assess local topic quality.
result Consistency metric correlates best with human judgments of local topic quality.
New metric R2 improves topic model evaluation.
problem Lack of standard cross-contextual evaluation metrics for topic modeling.
method Introduces R2 as a coefficient of determination for topic models. result Improves topic model evaluation by providing a standard metric.
TopicEq model generates equations and text from scientific papers.
problem Communicating ideas in scientific texts using both mathematics and text.
method Joint topic and equation generation model using correlated topic model and RNN.
result Joint model outperforms existing topic and equation models for scientific texts.
Proposes HMHP for joint modeling of user-topic interactions.
problem Complex interactions between users, topics and time on social media.
method Hidden Markov Hawkes Process (HMHP) incorporating topical Markov Chains.
result HMHP outperforms state-of-the-art models in generalization and accuracy.
Topic-aware chatbot learns from NMF topic vectors.
problem Improving chatbot relevance based on user topics.
method Combines RNN with NMF for topic learning and attention.
result Chatbot provides more relevant answers based on topic.
Neural topic models improve topic discovery in large corpora.
problem Topic discovery in large text corpora.
method Neural auto-encoder based document embedding with PLSA assumptions.
result Neural topic models outperform LDA in capturing relevant topics and perplexity.
We introduce Gaussian Process Topic Models (GPTMs), a new family of topic models which can leverage a kernel among documents while extracting correlated topics. GPTMs can be considered a systematic generalization of the Correlated Topic Models (CTMs) using ideas from Gaussian Process (GP) based embedding. Since GPTMs w…
New Gamma-Poisson model improves topic selection for short text.
problem Topic modelling for short text using Poisson distribution.
method Gamma-Poisson mixture model with collapsed Gibbs sampler.
result Gamma-Poisson model selects more accurate number of topics.
We propose a parsimonious topic model for text corpora. In related models such as Latent Dirichlet Allocation (LDA), all words are modeled topic-specifically, even though many words occur with similar frequencies across different topics. Our modeling determines salient words for each topic, which have topic-specific pr…
jLDADMM offers Java tools for topic modeling on texts.
problem Topic modeling on texts, especially short texts.
method Collaborated Gibbs sampling for LDA and DMM models.
result Alternative topic modeling solutions for normal or short texts.
Optimizes topic number in topic models using Rényi and Tsallis entropies.
problem Determining the optimal number of topics in topic models for large textual collections.
method Uses Rényi and Tsallis entropies based on thermodynamics approach.
result Semantic stability plays a crucial role in determining the optimal number of topics.
Improved neural topic model for semi-supervised learning.
problem Representing textual data in an interpretable manner with limited labeled data.
method Label-Indexed Neural Topic Model (LI-NTM) that combines deep generative models with semi-supervised learning.
result LI-NTM outperforms existing models in document reconstruction and classifier performance.
A dynamic keyword selection model for topic modeling of tweets.
problem Adjusting keywords dynamically to mimic past topics with novelty.
method Generative process selects keywords and documents, trained with variational lower bound and stochastic gradient optimization.
result Keyword-based topic model outperforms a sophisticated baseline model by 67%.
Paper combines citation network and author-topic models for bibliographic analysis.
problem Improving bibliographic analysis of author's research areas and paper content.
method Non-parametric extension of Poisson mixed-topic link model and author-topic model with efficient inference algorithm.
result Improved performance in model fitting and clustering compared to baselines.
We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial distribution over words.…
A new neural topic model using optimal transport improves document representation and topic coherence.
problem Challenges in achieving good document representation and coherent/diverse topics in existing NTMs.
method Proposes a neural topic model via optimal transport, learning topic distribution by minimising OT distance to document word distributions.
result Significantly outperforms state-of-the-art NTMs on discovering coherent and diverse topics.
E-LDA offers faster, interpretable LDA topic models.
problem Inferring topics in LDA topic models with strong guarantees.
method Non-gradient combinatorial approach for faster convergence.
result Logarithmic parallel computation time and interpretability.
SenGen generates sentences conditioned on topics, improving topic visualization.
problem Improve topic visualization and interpretability in documents.
method Variational auto-encoder with RNN decoder conditioned on topics.
result Preliminary experiments show promise but also challenges remain.
CFTM uses fractional Brownian motion for dynamic topic modeling.
problem Identifying long-term dependency or roughness in topic and word distributions over time.
method Continuous Time Fractional Topic Model (cFTM) incorporating fractional Brownian motion.
result cFTM captures long-term dependency or roughness in topic and word distributions.
Faster, context-aware topic modeling approach unveiled.
problem Slow and heavy preprocessing in topic modeling.
method Conceptualizes topics as independent axes, decomposes embeddings using ICA.
result 4.5x faster than BERTopic on average, with diverse and coherent topics.
New algorithms for topic modeling using conic geometry.
problem Unknown number of topics in nonparametric topic modeling.
method Analysis of topic simplex's concentration and angular geometry.
result Accuracy comparable to Gibbs sampler, fast compared to state-of-the-art techniques.
A scalable topic model for large document collections using MapReduce.
problem Scalability issues in topic modeling for large document collections.
method Correlated Topic Model with variational Expectation-Maximization in MapReduce framework.
result Comparable topic coherences with LDA in MapReduce framework.
Predicts EHR features that improve prediction, learning coherent topics.
problem Balancing prediction quality and coherence in EHR topic models.
method Prediction-focused topic model that uses supervisory signal to retain relevant features.
result Prediction-focused topic models learn more coherent topics while maintaining competitive predictions.
New NMF algorithms improve topic model fits.
problem Improving topic model fits for large datasets.
method Leveraging recent NMF optimization methods to fit topic models efficiently.
result Better topic model fits and faster computation times.
Paper compares NMF and LDA for topic labeling in customer communications.
problem Automatically labeling topics in customer inquiries.
method Uses NMF and LDA for topic mining and labeling.
result Proposes methods for automated topic subject labeling.
This paper improves topic modeling by embedding words and topics together.
problem Topic models struggle with short documents and approximate inference.
method Model each document as a mixture of word embeddings and each topic as a mixture of topic embeddings.
result The method optimizes topic embeddings to minimize semantic differences between words and topics.
Paper proposes a Renyi entropy-based method for tuning hierarchical topic models.
problem Tuning hierarchical topic models, especially determining the number of topics at each level, is challenging.
method The paper introduces a Renyi entropy-based metric for quality assessment and a practical tuning concept.
result The proposed method can estimate the number of topics for two hierarchical levels in hARTM model.
Bayesian method models dynamic text networks and topics.
problem Discovering community structure and topics in evolving text networks.
method Bayesian approach combining network and topic modeling.
result Complex community structure identified, dependent on blogger interests.
New topic model uses combinatorial optimization for faster, better results.
problem Improving topic modeling efficiency and quality.
method Derived a new objective function from LDA by small-variance asymptotics, solved combinatorially.
result New algorithm outperforms existing probabilistic methods.
A new topic modeling method that minimizes topic simplex volume.
problem Topic modeling efficiency and accuracy.
method Reformulates LDA as minimizing topic simplex volume, uses convex relaxation and ADMM.
result Relaxed problem has same global minimum as original under assumptions.
Combines topic modelling and Rasch modelling for MOOC student assessment.
problem Discovering meaningful topics that fit the Rasch model for MOOC student abilities.
method Combines Rasch model with non-negative matrix factorisation for topic discovery.
result Demonstrated suitability of the approach on MOOC data.
Geometric method captures rare topics and temporal alignment in co-author networks.
problem Missing rare topics and smooth temporal alignment in topic modeling.
method Integrates multimodal text and co-author network data using Hellinger distances and Ward's linkage.
result Effective identification of rare topics and visualization of topic drift over time.
nnLDA combines neural and probabilistic methods for better topic modeling with side information.
problem Lack of integration of auxiliary information in traditional topic models.
method nnLDA integrates side information through a neural prior mechanism, optimizing both neural and probabilistic components.
result nnLDA outperforms traditional models in topic coherence, perplexity, and classification.
Neural model predicts survival outcomes and reveals feature relationships.
problem Predicting time-to-event outcomes and understanding feature relationships in clinical data.
method Survival and topic modeling combined in a neural network framework.
result Neural survival-supervised topic models achieve competitive accuracy with interpretability.
Interactive topic models with user-defined anchor words.
problem Building interpretable topic models.
method Protocol allowing users to interactively define anchor words for topic modeling.
result Validation of the usefulness of interactive topic models.
A new method for topic detection using hierarchical latent tree models.
problem Hierarchical topic detection in document collections.
method Graphical models (HLTMs) with binary variables at different levels representing word co-occurrence patterns and document clusters.
result Captures both general and specific topics at different levels of a hierarchical structure.