Papers with Topic Modeling
ITMT: Interactive Topic Model Trainer (2023.eacl-demo)
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| Challenge: | Topic Modeling is a commonly used technique for analyzing unstructured data, but achieving accurate results and useful models can be challenging. |
| Approach: | They propose to use an interactive topic model trainer to train and curation topic extraction libraries and compare it with other tools for topic modeling analysis. |
| Outcome: | The proposed tool is compared with other tools for topic modeling analysis. |
Automating Qualitative Data Analysis with Large Language Models (2024.acl-srw)
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| Challenge: | Existing methods for qualitative data analysis are far from resembling a human's analysis outcome. |
| Approach: | They propose a method based on Large Language Models to tackle automated coding and make it as close as possible to the results of human researchers. |
| Outcome: | The proposed method is based on large language models and can be as close as possible to the results of human researchers. |
Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation (N18-1)
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| Challenge: | Existing models for user reviews are limited by data sparsity and lack of data. |
| Approach: | They propose to integrate LSTM and Topic Modeling to extract review information for recommender systems by utilizing user reviews. |
| Outcome: | The proposed model outperforms existing models on Amazon review dataset and shows better ability on making topic clustering than traditional topic model based method. |
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian (2020.lrec-1)
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Seyed Arad Ashrafi Asli, Behnam Sabeti, Zahra Majdabadi, Preni Golazizian, Reza Fahmi, Omid Momenzadeh
| Challenge: | Existing deep learning approaches require huge amounts of data to be trained properly. |
| Approach: | They propose to use Persian as a model to choose the samples for annotation instead of labeling the whole dataset. |
| Outcome: | The proposed models achieve the baseline performance with a significantly lower amount of labeled data. |
Topic Modeling for Short Texts via Optimal Transport-Based Clustering (2025.findings-acl)
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| Challenge: | Existing approaches to topic modeling are based on probabilistic graphical models or non-negative matrix factorization techniques. |
| Approach: | They propose a method that aligns global clusters with topics to discover topics and learn document representations in topic space. |
| Outcome: | The proposed method outperforms state-of-the-art techniques in short-text topic modeling across commonly used metrics. |
Neural Topic Modeling with Cycle-Consistent Adversarial Training (2020.emnlp-main)
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| Challenge: | Recent advances on deep generative models have attracted significant interest in neural topic modeling. |
| Approach: | They propose an adversarial-neural topic model which uses Dirichlet prior to capture the semantic patterns in latent topics. |
| Outcome: | The proposed models outperform competing models on unsupervised/supervised topic modeling and text classification. |
LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings (2025.coling-main)
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| Challenge: | Sentence embedding models are limited for many low-resource languages, including Luxembourgish. |
| Approach: | They propose to use Luxembourgish as an enhanced sentence embedding model with strong cross-lingual capabilities to address this issue. |
| Outcome: | The proposed model can embed Luxembourgish sentences better than high-resource languages. |
S2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling (2025.acl-long)
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| Challenge: | Variational autoencoder-based neural topic models lack posterior collapse in high-dimensional text data. |
| Approach: | They propose a Spherical Sliced Wasserstein Autoencoder for Topic Modeling to model latent representations in a hyperspherical space. |
| Outcome: | The proposed model outperforms state-of-the-art topic models while improving performance on downstream tasks. |
From Noise to Clarity: Filtering Real and LLM-Generated Samples for Enhanced Intent Detection (2025.findings-emnlp)
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| Challenge: | Using large language models for generating synthetic samples for data augmentation can cause problems with the generalization ability of classification models. |
| Approach: | They propose an interpretable Sample Filter by Topic Modeling framework that allows for filtering by topic and a 'sampler by topic' framework. |
| Outcome: | The proposed framework reduces the quantity of real and synthetic samples while improving the performance of the classification models. |