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Improving Topic Models with Latent Feature Word Representations

arXiv 1810.06306 ↗First posted 15 Oct 2018cs.CL cs.IR cs.LGcited by 28

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Abstract (arXiv)

Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document clustering and document classification tasks, especially on datasets with few or short documents.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Oct 2018.

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Citing papers per year: 2021: 7; 2022: 10; 2023: 6; 2024: 2; 2025: 3.

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  1. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  2. 2021Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherencecited by 74
  3. 2021Topic Modelling Meets Deep Neural Networks: A Surveycited by 51
  4. 2022Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddingscited by 36
  5. 2022Topic Discovery via Latent Space Clustering of Pretrained Language Model Representationscited by 34
  6. 2023vONTSS: vMF based semi-supervised neural topic modeling with optimal transportcited by 16
  7. 2023DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLMcited by 16
  8. 2022HyperMiner: Topic Taxonomy Mining with Hyperbolic Embeddingcited by 15
  9. 2022Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contextscited by 12
  10. 2022Seed-Guided Topic Discovery with Out-of-Vocabulary Seedscited by 7
  11. 2023CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modelingcited by 7
  12. 2021Multi-source Neural Topic Modeling in Multi-view Embedding Spacescited by 4
16 more
  1. 2022DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentationcited by 4
  2. 2021Short Text Clustering with Transformerscited by 3
  3. 2021Evaluation of Thematic Coherence in Microblogscited by 3
  4. 2024Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsingcited by 2
  5. 2022Enhance Topics Analysis based on Keywords Propertiescited by 1
  6. 2022Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Frameworkcited by 1
  7. 2022Chronic pain patient narratives allow for the estimation of current pain intensitycited by 1
  8. 2023Efficient and Flexible Topic Modeling using Pretrained Embeddings and Bag of Sentencescited by 1
  9. 2023Towards Generalising Neural Topical Representationscited by 1
  10. 2025Toward Purpose-oriented Topic Model Evaluation enabled by Large Language Modelscited by 1
  11. 2021Chronic Pain and Language: A Topic Modelling Approach to Personal Pain Descriptionscited by 0
  12. 2021Bilingual Topic Models for Comparable Corporacited by 0
  13. 2023How People Respond to the COVID-19 Pandemic on Twitter: A Comparative Analysis of Emotional Expressions from US and Indiacited by 0
  14. 2024A Novel Method for News Article Event-Based Embeddingcited by 0
  15. 2025Fine-Tuning Topics through Weighting Aspect Keywordscited by 0
  16. 2025Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOMcited by 0