Papers Topic Models
“Topic Models” 태그가 달린 논문 928편 · 필터 해제
Labeled Interactive Topic Models
Topic models are valuable for understanding extensive document collections, but they don't always identify the most relevant topics. Classical probabilistic and anchor-based topic models offer interactive versions that a…
Topic ModelsProfiling Irony & Stereotype: Exploring Sentiment, Topic, and Lexical Features
Social media has become a very popular source of information. With this popularity comes an interest in systems that can classify the information produced. This study tries to create such a system detecting irony in Twit…
feature selectionTopic ModelsTopicGPT: A Prompt-based Topic Modeling Framework
Topic modeling is a well-established technique for exploring text corpora. Conventional topic models (e.g., LDA) represent topics as bags of words that often require "reading the tea leaves" to interpret; additionally, t…
SpecificityTopic ModelsLet the Pretrained Language Models "Imagine" for Short Texts Topic Modeling
Topic models are one of the compelling methods for discovering latent semantics in a document collection. However, it assumes that a document has sufficient co-occurrence information to be effective. However, in short te…
Text GenerationTopic ModelsDeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM
In the burgeoning field of natural language processing (NLP), Neural Topic Models (NTMs) , Large Language Models (LLMs) and Diffusion model have emerged as areas of significant research interest. Despite this, NTMs prima…
DecoderText GenerationTopic ModelsResolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation
In addressing the imbalanced issue of data within the realm of Natural Language Processing, text data augmentation methods have emerged as pivotal solutions. This data imbalance is prevalent in the research proposals sub…
Data AugmentationFairnessTopic ModelsTopicAdapt- An Inter-Corpora Topics Adaptation Approach
Topic models are popular statistical tools for detecting latent semantic topics in a text corpus. They have been utilized in various applications across different fields. However, traditional topic models have some limit…
Topic ModelsTowards the TopMost: A Topic Modeling System Toolkit
Topic models have a rich history with various applications and have recently been reinvigorated by neural topic modeling. However, these numerous topic models adopt totally distinct datasets, implementations, and evaluat…
Topic ModelsVariational InferenceEvaluating Dynamic Topic Models
There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs). Filling this gap, we propose a novel evaluation measure for DTMs that analyzes the changes in th…
Topic ModelsTowards Generalising Neural Topical Representations
Topic models have evolved from conventional Bayesian probabilistic models to recent Neural Topic Models (NTMs). Although NTMs have shown promising performance when trained and tested on a specific corpus, their generalis…
Data AugmentationTopic ModelsLarge-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization
Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving…
Dimensionality ReductionSemantic SimilaritySemantic Textual SimilarityTopic ModelsvONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Recently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real world due to the challenge of incorporati…
ClassificationDiversitytext-classificationText Classification+2Effective Neural Topic Modeling with Embedding Clustering Regularization
Topic models have been prevalent for decades with various applications. However, existing topic models commonly suffer from the notorious topic collapsing: discovered topics semantically collapse towards each other, lead…
ClusteringTopic ModelsPainsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews
As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers. Previous studies have endea…
Opinion MiningSentiment AnalysisTopic ModelsDiversity-Aware Coherence Loss for Improving Neural Topic Models
The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, in addition to the reconstruction loss. S…
DiversityTopic ModelsContextualized Topic Coherence Metrics
The recent explosion in work on neural topic modeling has been criticized for optimizing automated topic evaluation metrics at the expense of actual meaningful topic identification. But human annotation remains expensive…
Topic ModelsRevisiting Automated Topic Model Evaluation with Large Language Models
Topic models are used to make sense of large text collections. However, automatically evaluating topic model output and determining the optimal number of topics both have been longstanding challenges, with no effective a…
Topic ModelsCWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling
Most existing topic models rely on bag-of-words (BOW) representation, which limits their ability to capture word order information and leads to challenges with out-of-vocabulary (OOV) words in new documents. Contextualiz…
Document ClassificationLanguage ModellingNatural Language UnderstandingNER+3HyHTM: Hyperbolic Geometry based Hierarchical Topic Models
Hierarchical Topic Models (HTMs) are useful for discovering topic hierarchies in a collection of documents. However, traditional HTMs often produce hierarchies where lowerlevel topics are unrelated and not specific enoug…
Topic ModelsReinforcement Learning for Topic Models
We apply reinforcement learning techniques to topic modeling by replacing the variational autoencoder in ProdLDA with a continuous action space reinforcement learning policy. We train the system with a policy gradient al…
DiversityReinforcement LearningReinforcement Learning (RL)Sentence Embeddings+1