Dynamic Embedded Topic Models: properties and recommendations based on diverse corpora
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Abstract (arXiv)
We measure the effects of several implementation choices for the Dynamic Embedded Topic Model, as applied to five distinct diachronic corpora, with the goal of isolating important decisions for its use and further development. We identify priorities that will maximize utility in applied scholarship, including the practical scalability of vocabulary size to best exploit the strengths of embedded representations, and more flexible modeling of intervals to accommodate the uneven temporal distributions of historical writing. Of similar importance, we find performance is not significantly or consistently affected by several aspects that otherwise limit the model's application or might consume the resources of a grid search.
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References · 9
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2019Topic Modeling in Embedding Spacescited by 224
- 2021Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherencecited by 74
- 2016Efficient softmax approximation for GPUscited by 67
- 2019The Dynamic Embedded Topic Modelcited by 41
- 2018Augment and Reduce: Stochastic Inference for Large Categorical Distributionscited by 3
- 2023Computational modeling of semantic changecited by 2
- 2024Dynamic embedded topic models and change-point detection for exploring literary-historical hypothesescited by 2
- 2020Anchor & Transform: Learning Sparse Embeddings for Large Vocabulariescited by 1
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