Papers with Topic Modeling
DynaMiTE: Discovering Explosive Topic Evolutions with User Guidance (2023.findings-acl)
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| Challenge: | Existing Dynamic topic models are either fully supervised, requiring expensive human annotations, or fully unsupervised, producing topic evolutions that often do not cater to a user’s needs. |
| Approach: | They propose to use a framework that ensembles semantic similarity, category indicative, and time indicative scores to produce informative topic evolutions. |
| Outcome: | The proposed framework can be used to discover topic evolutions from temporal corpora that align with user-provided category names and uniquely capture topics at each time step. |
CASE: Large Scale Topic Exploitation for Decision Support Systems (2025.coling-demos)
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| Challenge: | Topic models are still a major tool for information retrieval and summarization, but their integration into decision-making systems is limited. |
| Approach: | They propose a tool for exploiting topic information for semantic analysis of large corpora using a Solr engine and a customized indexing strategy. |
| Outcome: | The proposed approach can be used to analyze large corpora and perform thematic trend analysis, topic-based document retrieval, or similarity search. |
S3 - Semantic Signal Separation (2025.acl-long)
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Márton Kardos, Jan Kostkan, Kenneth Enevoldsen, Arnault-Quentin Vermillet, Kristoffer Nielbo, Roberta Rocca
| Challenge: | Recent efforts to incorporate contextual representations into topic models have been shown to outperform classical topic models. |
| Approach: | They propose a theory-driven topic modeling approach that decomposes contextualized document embeddings into a Python package that implements S3 and all contextual baselines. |
| Outcome: | The proposed model is 4.5x faster than the BERTopic model and provides diverse and highly coherent topics with no preprocessing. |
Deep Dirichlet Multinomial Regression (N18-1)
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| Challenge: | supervised topic models can incorporate arbitrary document-level features to inform topic priors, but their ability to model corpora is limited by the representation and selection of these features. |
| Approach: | They propose a generative topic model that simultaneously learns document feature representations and topics. |
| Outcome: | The proposed model outperforms DMR and LDA on three datasets and human subjects judge it more representative of associated document features. |
Infinite SCAN: An Infinite Model of Diachronic Semantic Change (2022.emnlp-main)
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| Challenge: | Existing methods for capturing semantic changes using word embeddings cannot account for existence of each sense and its relative importance. |
| Approach: | They propose a Bayesian model that can estimate the number of senses of words and their changes through time using a dynamic topic model and a logistic stick-breaking process. |
| Outcome: | The proposed model outperforms the baseline model and investigates the semantic changes of several well-known target words using the CCOHA corpus. |
Stock Movement Prediction from Tweets and Historical Prices (P18-1)
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| Challenge: | a novel deep generative model exploits text and price signals to make stochastic stock movement predictions. |
| Approach: | They propose a deep generative model exploiting text and price signals to solve this problem. |
| Outcome: | The proposed model exploits text and price signals to make temporally-dependent predictions from chaotic data. |
Neural Sparse Topical Coding (P18-1)
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| Challenge: | Topic models with sparsity enhancement are effective at learning discriminative and coherent latent topics of short texts. |
| Approach: | They propose a novel sparsity-enhanced topic model with back propagation that replaces the inference process with the back propagations, making it easy to explore extensions. |
| Outcome: | The proposed model outperforms existing methods on Web Snippet and 20Newsgroups datasets. |
Cross-referencing Using Fine-grained Topic Modeling (N19-1)
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| Challenge: | Cross-referencing is a useful study aid for facilitating comprehension of a text, but it requires extensive thematic knowledge and a focused search through the corpus to find such useful connections. |
| Approach: | They propose a system for producing candidate cross-references which can be easily verified by human annotators. |
| Outcome: | a new system can produce cross-references that can be easily verified by human annotators . the system uses fine-grained topic modeling to identify verse pairs which are topically related . |
Modeling Online Discourse with Coupled Distributed Topics (D18-1)
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| Challenge: | a topic model that incorporates structural relationships connecting documents in socially generated corpora is of limited application in the sciences. |
| Approach: | They propose a topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. |
| Outcome: | The proposed model captures discursive interactions along observed reply links and integrates latent distributed representations in a deep architecture. |
Coordinated Topic Modeling (2022.emnlp-main)
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| Challenge: | Rather than finding arbitrary topics, people often want to explore the text based on some welldefined topics. |
| Approach: | They propose a problem called coordinated topic modeling that imitates human behavior while describing a text corpus. |
| Outcome: | The proposed model is superior to baseline models on multiple domains. |
Topic Modeling With Topological Data Analysis (2022.emnlp-main)
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| Challenge: | Recent topic modelling approaches that use clustering on word, token or document embeddings can ex-tract coherent topics. |
| Approach: | They propose an unsupervised topic mod-elling method which uses TopologicalData Analysis to extract a topologicalskeleton of the manifold upon which word embeddings lie. |
| Outcome: | The proposed method performs on par with a baseline and can construct a network of coherent topics with meaningful relationships between them. |