Results 71 to 80 of about 1,143,739 (216)
Towards Understanding Community Interests With Topic Modeling
IEEE Access, 2018 Community plays an important role in shaping a network. Quantitatively interpreting a community is necessary for graph generalization which is used for privacy preserving, summarization, and dimensionality reduction in social network mining.
Feng Wang +3 more
doaj +1 more source
A novel multiple kernel fuzzy topic modeling technique for biomedical data
BMC Bioinformatics, 2022 Background Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format.
Junaid Rashid +4 more
doaj +1 more source
Frontiers in Genetics, 2023 With the exponential growth in the daily publication of scientific articles, automatic classification and categorization can assist in assigning articles to a predefined category.
Daniel Voskergian +3 more
doaj +1 more source
Redundancy-aware topic modeling for patient record notes. [PDF]
PLoS ONE, 2014 The clinical notes in a given patient record contain much redundancy, in large part due to clinicians' documentation habit of copying from previous notes in the record and pasting into a new note.
Raphael Cohen +3 more
doaj +1 more source
Neural Topic Modeling with Cycle-Consistent Adversarial Training [PDF]
arXiv, 2020 Advances on deep generative models have attracted significant research interest in neural topic modeling. The recently proposed Adversarial-neural Topic Model models topics with an adversarially trained generator network and employs Dirichlet prior to capture the semantic patterns in latent topics.
arxiv
, 2009 In this thesis I will present my PhD research work, focusing mainly on financial modelling of asset’s volatility and the pricing of contingent claims (financial derivatives), which consists of four topics: 1. Several changing volatility models are introduced and the pricing of European options is derived under these models; 2.
Yi, Cong, Yi, Cong
openaire +4 more sources
An Automatic Approach for Document-level Topic Model Evaluation [PDF]
arXiv, 2017 Topic models jointly learn topics and document-level topic distribution. Extrinsic evaluation of topic models tends to focus exclusively on topic-level evaluation, e.g. by assessing the coherence of topics. We demonstrate that there can be large discrepancies between topic- and document-level model quality, and that basing model evaluation on topic ...
arxiv
Labeled Interactive Topic Models [PDF]
arXiv, 2023 Topic models are valuable for understanding extensive document collections, but they don't always identify the most relevant topics. Classical probabilistic and anchor-based topic models offer interactive versions that allow users to guide the models towards more pertinent topics.
arxiv
Improving Neural Topic Models using Knowledge Distillation [PDF]
arXiv, 2020 Topic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers. Our modular method can be straightforwardly applied with any neural topic model to improve topic quality, which we ...
arxiv
Generating Video Descriptions with Topic Guidance [PDF]
arXiv, 2017 Generating video descriptions in natural language (a.k.a. video captioning) is a more challenging task than image captioning as the videos are intrinsically more complicated than images in two aspects. First, videos cover a broader range of topics, such as news, music, sports and so on. Second, multiple topics could coexist in the same video.
arxiv