Papers Topic Models
“Topic Models” 태그가 달린 논문 928편 · 필터 해제
NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization
Recent advances in neural topic models have concentrated on two primary directions: the integration of the inference network (encoder) with a pre-trained language model (PLM) and the modeling of the relationship between …
DecoderLanguage ModelingLanguage ModellingTopic ModelsInvestigating the Impact of Text Summarization on Topic Modeling
Topic models are used to identify and group similar themes in a set of documents. Recent advancements in deep learning based neural topic models has received significant research interest. In this paper, an approach is p…
DiversityLanguage ModelingLanguage ModellingLarge Language Model+2Iterative Improvement of an Additively Regularized Topic Model
Topic modelling is fundamentally a soft clustering problem (of known objects -- documents, over unknown clusters -- topics). That is, the task is incorrectly posed. In particular, the topic models are unstable and incomp…
modelTopic ModelsTopic Modeling with Fine-tuning LLMs and Bag of Sentences
Large language models (LLM)'s are increasingly used for topic modeling outperforming classical topic models such as LDA. Commonly, pre-trained LLM encoders such as BERT are used out-of-the-box despite the fact that fine-…
SentenceTopic ModelsExchangeable Sequence Models Quantify Uncertainty Over Latent Concepts
Intelligent agents must be able to articulate its own uncertainty. In this work, we show that pre-trained sequence models are naturally capable of probabilistic reasoning over exchangeable data points -- forming informed…
Bayesian InferenceData AugmentationIn-Context LearningTopic Models+2TopicTag: Automatic Annotation of NMF Topic Models Using Chain of Thought and Prompt Tuning with LLMs
Topic modeling is a technique for organizing and extracting themes from large collections of unstructured text. Non-negative matrix factorization (NMF) is a common unsupervised approach that decomposes a term frequency-i…
Knowledge GraphsManagementPrompt EngineeringTopic ModelsA Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations
The semantic similarity between documents of a text corpus can be visualized using map-like metaphors based on two-dimensional scatterplot layouts. These layouts result from a dimensionality reduction on the document-ter…
DescriptiveDimensionality ReductionSemantic SimilaritySemantic Textual Similarity+2Text-to-Battery Recipe: A language modeling-based protocol for automatic battery recipe extraction and retrieval
Recent studies have increasingly applied natural language processing (NLP) to automatically extract experimental research data from the extensive battery materials literature. Despite the complex process involved in batt…
Information RetrievalLanguage ModelingLanguage Modellingnamed-entity-recognition+2Interactive Topic Models with Optimal Transport
Topic models are widely used to analyze document collections. While they are valuable for discovering latent topics in a corpus when analysts are unfamiliar with the corpus, analysts also commonly start with an understan…
Topic ModelsHistoria Magistra Vitae: Dynamic Topic Modeling of Roman Literature using Neural Embeddings
Dynamic topic models have been proposed as a tool for historical analysis, but traditional approaches have had limited usefulness, being difficult to configure, interpret, and evaluate. In this work, we experiment with a…
Dynamic Topic ModelingTopic ModelsCutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination
The COVID-19 pandemic exposed significant weaknesses in the healthcare information system. The overwhelming volume of misinformation on social media and other socioeconomic factors created extraordinary challenges to mot…
MisinformationStance DetectionText ClassificationTopic ModelsDetermination of the Number of Topics Intrinsically: Is It Possible?
The number of topics might be the most important parameter of a topic model. The topic modelling community has developed a set of various procedures to estimate the number of topics in a dataset, but there has not yet be…
Topic ModelsLLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models
Topic modeling has been a widely used tool for unsupervised text analysis. However, comprehensive evaluations of a topic model remain challenging. Existing evaluation methods are either less comparable across different m…
Language ModelingLanguage ModellingTopic Models$S^3$ -- Semantic Signal Separation
Topic models are useful tools for discovering latent semantic structures in large textual corpora. Recent efforts have been oriented at incorporating contextual representations in topic modeling and have been shown to ou…
blind source separationTopic ModelsRICo: Reddit ideological communities
The main objective of our research is to gain a comprehensive understanding of the relationship between language usage within different communities and delineating the ideological narratives. We focus specifically on uti…
ArticlesNews ClassificationNews GenerationNews Recommendation+3Comprehensive Evaluation of Large Language Models for Topic Modeling
Recent work utilizes Large Language Models (LLMs) for topic modeling, generating comprehensible topic labels for given documents. However, their performance has mainly been evaluated qualitatively, and there remains room…
HallucinationTopic ModelsFASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model
Topic models have been evolving rapidly over the years, from conventional to recent neural models. However, existing topic models generally struggle with either effectiveness, efficiency, or stability, highly impeding th…
RelationTopic ModelsWord EmbeddingsModeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion
Dynamic topic models track the evolution of topics in sequential documents, which have derived various applications like trend analysis and opinion mining. However, existing models suffer from repetitive topic and unasso…
Contrastive LearningDiversityOpinion MiningTopic ModelsExploring Public Attention in the Circular Economy through Topic Modelling with Twin Hyperparameter Optimisation
To advance the circular economy (CE), it is crucial to gain insights into the evolution of public attention, cognitive pathways of the masses concerning circular products, and to identify primary concerns. To achieve thi…
Dynamic Topic ModelingHyperparameter OptimizationTopic ModelsWord EmbeddingsConcept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM
Data analysts have long sought to turn unstructured text data into meaningful concepts. Though common, topic modeling and clustering focus on lower-level keywords and require significant interpretative work. We introduce…
Topic Models