Results 61 to 70 of about 975,825 (180)
How Many Topics? Stability Analysis for Topic Models
, 2014 Topic modeling refers to the task of discovering the underlying thematic structure in a text corpus, where the output is commonly presented as a report of the top terms appearing in each topic. Despite the diversity of topic modeling algorithms that have
C. Lin +13 more
core +1 more source
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 Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation [PDF]
, 2013 Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to perform scene recognition and annotation. Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was ...
Larochelle, Hugo +2 more
core
Fuzzy Approach Topic Discovery in Health and Medical Corpora
, 2017 The majority of medical documents and electronic health records (EHRs) are in text format that poses a challenge for data processing and finding relevant documents.
Gangopadhyay, Aryya +3 more
core +1 more source
Scientific ReportsTextNetTopics (Yousef et al. in Front Genet 13:893378, 2022. https://doi.org/10.3389/fgene.2022.893378 ) is a recently developed approach that performs text classification-based topics (a topic is a group of terms or words) extracted from a Latent ...
Daniel Voskergian +2 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
Deep Belief Nets for Topic Modeling [PDF]
, 2015 Applying traditional collaborative filtering to digital publishing is challenging because user data is very sparse due to the high volume of documents relative to the number of users.
Arngren, Morten +2 more
core +1 more source
Jointly Modeling Topics and Intents with Global Order Structure
, 2015 Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words.
Chen, Bei +5 more
core +1 more source
Multilingual Dynamic Topic Model
Proceedings - Natural Language Processing in a Deep Learning World, 2019 Dynamic topic models (DTMs) capture the evolution of topics and trends in time series data. Current DTMs are applicable only to monolingual datasets. In this paper we present the multilingual dynamic topic model (ML-DTM), a novel topic model that combines DTM with an existing multilingual topic modeling method to capture crosslingual topics that evolve
Zosa, Elaine, Granroth-Wilding, Mark
openaire +2 more sources
A Topic Modeling Toolbox Using Belief Propagation [PDF]
, 2012 Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interests and touches on many important applications in text mining, computer vision and computational biology.
Zeng, Jia
core