Papers Topic Models
“Topic Models” 태그가 달린 논문 928편 · 필터 해제
GINopic: Topic Modeling with Graph Isomorphism Network
Topic modeling is a widely used approach for analyzing and exploring large document collections. Recent research efforts have incorporated pre-trained contextualized language models, such as BERT embeddings, into topic m…
Topic ModelsEnhanced Short Text Modeling: Leveraging Large Language Models for Topic Refinement
Crafting effective topic models for brief texts, like tweets and news headlines, is essential for capturing the swift shifts in social dynamics. Traditional topic models, however, often fall short in accurately represent…
Prompt EngineeringTopic ModelsNeural Multimodal Topic Modeling: A Comprehensive Evaluation
Neural topic models can successfully find coherent and diverse topics in textual data. However, they are limited in dealing with multimodal datasets (e.g., images and text). This paper presents the first systematic and c…
Topic ModelsDiscovering Latent Themes in Social Media Messaging: A Machine-in-the-Loop Approach Integrating LLMs
Grasping the themes of social media content is key to understanding the narratives that influence public opinion and behavior. The thematic analysis goes beyond traditional topic-level analysis, which often captures only…
Topic ModelsMembership Inference Attacks and Privacy in Topic Modeling
Recent research shows that large language models are susceptible to privacy attacks that infer aspects of the training data. However, it is unclear if simpler generative models, like topic models, share similar vulnerabi…
Topic ModelsAutomating the Information Extraction from Semi-Structured Interview Transcripts
This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts. Given the labor-intensive nature of traditional qualitative analysis …
Topic ModelsThe Geometric Structure of Topic Models
Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research…
Feature CompressionTopic ModelsTopic Modeling as Multi-Objective Contrastive Optimization
Recent representation learning approaches enhance neural topic models by optimizing the weighted linear combination of the evidence lower bound (ELBO) of the log-likelihood and the contrastive learning objective that con…
Contrastive LearningDiversityRepresentation LearningTopic ModelsRankSum An unsupervised extractive text summarization based on rank fusion
In this paper, we propose Ranksum, an approach for extractive text summarization of single documents based on the rank fusion of four multi-dimensional sentence features extracted for each sentence: topic information, se…
Extractive Text SummarizationSentenceSentence EmbeddingsText Summarization+1Network-based Topic Structure Visualization
In the real world, many topics are inter-correlated, making it challenging to investigate their structure and relationships. Understanding the interplay between topics and their relevance can provide valuable insights fo…
Topic ModelsImproving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis
Topic models are a popular tool for understanding text collections, but their evaluation has been a point of contention. Automated evaluation metrics such as coherence are often used, however, their validity has been que…
Topic ModelsCFTM: Continuous time fractional topic model
In this paper, we propose the Continuous Time Fractional Topic Model (cFTM), a new method for dynamic topic modeling. This approach incorporates fractional Brownian motion~(fBm) to effectively identify positive or negati…
ArticlesDynamic Topic Modelingmodelparameter estimation+1A Survey on Neural Topic Models: Methods, Applications, and Challenges
Topic models have been prevalent for decades to discover latent topics and infer topic proportions of documents in an unsupervised fashion. They have been widely used in various applications like text analysis and contex…
SurveyTopic ModelsDynamic embedded topic models and change-point detection for exploring literary-historical hypotheses
We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin. We demonstrate several methods …
Change Point DetectionTopic ModelsTopic Modelling: Going Beyond Token Outputs
Topic modelling is a text mining technique for identifying salient themes from a number of documents. The output is commonly a set of topics consisting of isolated tokens that often co-occur in such documents. Manual eff…
Topic ModelsShort-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model
Along with the rise of short-form videos, their mental impacts on viewers have led to widespread consequences, prompting platforms to predict videos' impact on viewers' mental health. Subsequently, they can take interven…
FormTopic ModelsVideo ClassificationDiscovering Significant Topics from Legal Decisions with Selective Inference
We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalised regressions and post-selection significance t…
Language ModelingLanguage ModellingTopic ModelsDynamic Topic Language Model on Heterogeneous Children's Mental Health Clinical Notes
Mental health diseases affect children's lives and well-beings which have received increased attention since the COVID-19 pandemic. Analyzing psychiatric clinical notes with topic models is critical to evaluating childre…
Language ModelingLanguage ModellingTopic ModelsContrastive News and Social Media Linking using BERT for Articles and Tweets across Dual Platforms
X (formerly Twitter) has evolved into a contemporary agora, offering a platform for individuals to express opinions and viewpoints on current events. The majority of the topics discussed on Twitter are directly related t…
ArticlesContrastive LearningTopic ModelsRevisiting Topic-Guided Language Models
A recent line of work in natural language processing has aimed to combine language models and topic models. These topic-guided language models augment neural language models with topic models, unsupervised learning metho…
Language ModelingLanguage ModellingTopic Models