Papers Topic Models
“Topic Models” 태그가 달린 논문 928편 · 필터 해제
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections
Experts across diverse disciplines are often interested in making sense of large text collections. Traditionally, this challenge is approached either by noisy unsupervised techniques such as topic models, or by following…
Topic ModelsTwo to Five Truths in Non-Negative Matrix Factorization
In this paper, we explore the role of matrix scaling on a matrix of counts when building a topic model using non-negative matrix factorization. We present a scaling inspired by the normalized Laplacian (NL) for graphs th…
Clusteringdocument understandingGraph ClusteringSpectral Graph Clustering+2Graph2topic: an opensource topic modeling framework based on sentence embedding and community detection
It has been reported that clustering-based topic models, which cluster high-quality sentence embeddings with an appropriate word selection method, can generate better topics than generative probabilistic topic models. Ho…
Community DetectionLanguage ModellingSentenceSentence Embedding+3InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling
Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing aligned latent topics. However, most existing methods suffer from producing repetitive topics that hinder further analysis and p…
Topic ModelsA User-Centered, Interactive, Human-in-the-Loop Topic Modelling System
Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still …
Topic ModelsTopics in the Haystack: Extracting and Evaluating Topics beyond Coherence
Extracting and identifying latent topics in large text corpora has gained increasing importance in Natural Language Processing (NLP). Most models, whether probabilistic models similar to Latent Dirichlet Allocation (LDA)…
SentenceTopic ModelsDo Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling
Dropout is a widely used regularization trick to resolve the overfitting issue in large feedforward neural networks trained on a small dataset, which performs poorly on the held-out test subset. Although the effectivenes…
DecoderTopic ModelsImproving Neural Topic Models with Wasserstein Knowledge Distillation
Topic modeling is a dominant method for exploring document collections on the web and in digital libraries. Recent approaches to topic modeling use pretrained contextualized language models and variational autoencoders. …
Knowledge DistillationTopic ModelsImproving Contextualized Topic Models with Negative Sampling
Topic modeling has emerged as a dominant method for exploring large document collections. Recent approaches to topic modeling use large contextualized language models and variational autoencoders. In this paper, we propo…
DiversityTopic ModelsTripletNo more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning
The number of papers submitted to academic conferences is steadily rising in many scientific disciplines. To handle this growth, systems for automatic paper-reviewer assignments are increasingly used during the reviewing…
Topic ModelsGraph neural topic model with commonsense knowledge
Traditional topic models are based on the bag-of-words assumption, which states that the topic assignment of each word is independent of the others. However, this assumption ignores the relationship between words, which …
Graph Neural NetworkmodelTopic ModelsFederated Variational Inference Methods for Structured Latent Variable Models
Federated learning methods enable model training across distributed data sources without data leaving their original locations and have gained increasing interest in various fields. However, existing approaches are limit…
Federated LearningTopic ModelsVariational InferenceANTM: An Aligned Neural Topic Model for Exploring Evolving Topics
This paper presents an algorithmic family of dynamic topic models called Aligned Neural Topic Models (ANTM), which combine novel data mining algorithms to provide a modular framework for discovering evolving topics. ANTM…
DiversityDynamic Topic ModelingTopic ModelsYou Are What You Talk About: Inducing Evaluative Topics for Personality Analysis
Expressing attitude or stance toward entities and concepts is an integral part of human behavior and personality. Recently, evaluative language data has become more accessible with social media's rapid growth, enabling l…
Topic ModelsNeural Dynamic Focused Topic Model
Topic models and all their variants analyse text by learning meaningful representations through word co-occurrences. As pointed out by Williamson et al. (2010), such models implicitly assume that the probability of a top…
modelTopic ModelsVariational InferenceImproving the Inference of Topic Models via Infinite Latent State Replications
In text mining, topic models are a type of probabilistic generative models for inferring latent semantic topics from text corpus. One of the most popular inference approaches to topic models is perhaps collapsed Gibbs sa…
Topic ModelsInterpretable and Scalable Graphical Models for Complex Spatio-temporal Processes
This thesis focuses on data that has complex spatio-temporal structure and on probabilistic graphical models that learn the structure in an interpretable and scalable manner. We target two research areas of interest: Gau…
EEGElectroencephalogram (EEG)Topic ModelsWeather ForecastingTopics in Contextualised Attention Embeddings
Contextualised word vectors obtained via pre-trained language models encode a variety of knowledge that has already been exploited in applications. Complementary to these language models are probabilistic topic models th…
ClusteringLanguage ModelingLanguage ModellingTopic ModelsTopics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural Networks
Topic models aim to reveal latent structures within a corpus of text, typically through the use of term-frequency statistics over bag-of-words representations from documents. In recent years, conceptual entities -- inter…
Graph Neural NetworkLanguage ModellingTopic ModelsFederated Neural Topic Models
Over the last years, topic modeling has emerged as a powerful technique for organizing and summarizing big collections of documents or searching for particular patterns in them. However, privacy concerns may arise when c…
DiversityTopic Models