Papers with Latent Dirichlet Allocation
Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers (D19-55)
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| Challenge: | Using coherence scores to choose topics, we test whether the results help us to understand user interests and concerns. |
| Approach: | They analyze user reviews from Best Buy US website for smart speakers to determine whether they provide useful information for product analysis. |
| Outcome: | The proposed models capture brand performance and differences and differentiate the market into two distinct groups with different properties. |
Neural Topic Modeling with Bidirectional Adversarial Training (2020.acl-main)
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| Challenge: | Recent studies have shown that neural topic models for automatic topic extraction avoid complicated mathematical derivations for model inference. |
| Approach: | They propose a bidirectional adversarial topic model which uses a generator and an encoder to infer topic distribution. |
| Outcome: | The proposed model outperforms baselines and competitive models in three benchmark corpora. |
No, you’re not alone: A better way to find people with similar experiences on Reddit (D19-55)
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| Challenge: | a probabilistic clustering algorithm can help users find posts that discuss experiences similar to their own . a recent study shows that probabilistic Clustering can yield a better performance than baseline clustering methods . |
| Approach: | They propose a probabilistic clustering algorithm that can help Reddit users find posts that discuss experiences similar to their own. |
| Outcome: | The proposed algorithm can find posts that discuss experiences similar to their own . it performs better than baseline clustering methods due to high runtime overhead . |
Improving Topic Quality by Promoting Named Entities in Topic Modeling (P18-2)
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| Challenge: | Using named entities as domain-specific terms for news-centric content has not been studied extensively. |
| Approach: | They propose to use named entities as domain-specific terms for news-centric content . they propose a weighting model that incorporates more named entities in topic descriptors . |
| Outcome: | The proposed model improves the quality of news-centric topics by including more named entities in the topic descriptors. |
Community Topic: Topic Model Inference by Consecutive Word Community Discovery (2022.coling-1)
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| Challenge: | a new topic modelling algorithm is developed to help researchers understand large corpora . community topic can be used to find coherent topics at various scales . |
| Approach: | They propose a topic-modeling algorithm that extracts communities from term co-occurrence networks and compares it with Latent Dirichlet Allocation and top2vec. |
| Outcome: | The proposed algorithm can find coherent topics at various scales. |
Learning VAE-LDA Models with Rounded Reparameterization Trick (2020.emnlp-main)
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| Challenge: | No reparameterization form of Dirichlet distributions is known to date for topic models . |
| Approach: | They propose a method to reparameterize Dirichlet distributions for the learning of VAE-LDA models by using a latent Dirichlets prior. |
| Outcome: | The proposed method outperforms existing neural topic models on benchmark datasets and on a synthetic dataset. |
RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual Data (2021.findings-emnlp)
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| Challenge: | Existing methods for generating time series on textual data are not efficient . |
| Approach: | They propose a rolling version of the Latent Dirichlet Allocation, called RollingLDA . they compute similarity of sequentially obtained topic and word distributions over consecutive time periods . |
| Outcome: | The proposed method is based on the popular model Latent Dirichlet Allocation . it is able to build time series consistent with previous states of the model . |
More Than Words: Collocation Retokenization for Latent Dirichlet Allocation Models (2022.findings-acl)
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| Challenge: | Latent Dirichlet Allocation models ingest words to discover their latent topics . but it is unclear how to achieve the best results for languages without marked word boundaries . |
| Approach: | They propose to use retokenization to merge frequent token ngrams into collocations in input to a Latent Dirichlet Allocation model. |
| Outcome: | The proposed model improves topic coherence and coherency in Chinese and Thai . the proposed model is more coherent and clearer than unmerged models . |
Neural Caption Generation for News Images (L18-1)
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| Challenge: | Existing methods for automatic caption generation of images are lacking in the field of image-related applications. |
| Approach: | They propose a method for automatically generating captions for news images . they propose several deep neural network architectures built upon Recurrent Neural Networks . |
| Outcome: | The proposed method outperforms a traditional method on a BBC News dataset using automatic evaluation and human evaluation. |
Large Language Models Struggle to Describe the Haystack without Human Help: A Social Science-Inspired Evaluation of Topic Models (2025.acl-long)
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Zongxia Li, Lorena Calvo-Bartolomé, Alexander Miserlis Hoyle, Paiheng Xu, Daniel Kofi Stephens, Juan Francisco Fung, Alden Dima, Jordan Lee Boyd-Graber
| Challenge: | a common use of NLP is to facilitate the understanding of large document collections. |
| Approach: | They propose to use large language models to replace probabilistic topic models in real-world applications. |
| Outcome: | The proposed model generates more human-readable topics and shows higher average win probabilities than traditional models for data exploration. |
GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model (D18-1)
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| Challenge: | Conventional topic models suffer different problems in different settings. |
| Approach: | They propose a novel way to model word-pairs named biterms in the whole corpus and a Graph Convolutional Networks (GCNs) with residual connections to extract transitive features from biterm. |
| Outcome: | The proposed model generates more coherent topics compared with previous methods. |