Topic modeling using latent Dirichlet allocation: A survey
We are not able to deal with a mammoth text corpus without summarizing them into a
relatively small subset. A computational tool is extremely needed to understand such a …
relatively small subset. A computational tool is extremely needed to understand such a …
Pre-training is a hot topic: Contextualized document embeddings improve topic coherence
Topic models extract groups of words from documents, whose interpretation as a topic
hopefully allows for a better understanding of the data. However, the resulting word groups …
hopefully allows for a better understanding of the data. However, the resulting word groups …
Neural topic model via optimal transport
Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained
increasingly research interest due to their promising results on text analysis. However, it is …
increasingly research interest due to their promising results on text analysis. However, it is …
Scholastic: Graphical human-AI collaboration for inductive and interpretive text analysis
Interpretive scholars generate knowledge from text corpora by manually sampling
documents, applying codes, and refining and collating codes into categories until …
documents, applying codes, and refining and collating codes into categories until …
MetaLDA: A topic model that efficiently incorporates meta information
Besides the text content, documents and their associated words usually come with rich sets
of meta information, such as categories of documents and semantic/syntactic features of …
of meta information, such as categories of documents and semantic/syntactic features of …
Hierarchical Deep Document Model
Topic modeling is a commonly used text analysis tool for discovering latent topics in a text
corpus. However, while topics in a text corpus often exhibit a hierarchical structure (eg …
corpus. However, while topics in a text corpus often exhibit a hierarchical structure (eg …
A knowledge graph enhanced topic modeling approach for herb recommendation
X Wang, Y Zhang, X Wang, J Chen - … 2019, Chiang Mai, Thailand, April 22 …, 2019 - Springer
Abstract Traditional Chinese Medicine (TCM) plays an important role in Chinese society and
is an increasingly popular therapy around the world. A data-driven herb recommendation …
is an increasingly popular therapy around the world. A data-driven herb recommendation …
Presyndromic surveillance for improved detection of emerging public health threats
M Nobles, R Lall, RW Mathes, DB Neill - Science Advances, 2022 - science.org
Existing public health surveillance systems that rely on predefined symptom categories, or
syndromes, are effective at monitoring known illnesses, but there is a critical need for …
syndromes, are effective at monitoring known illnesses, but there is a critical need for …
A word embeddings informed focused topic model
In natural language processing and related fields, it has been shown that the word
embeddings can successfully capture both the semantic and syntactic features of words …
embeddings can successfully capture both the semantic and syntactic features of words …
Bert-based latent semantic analysis (Bert-LSA): a case study on geospatial data technology and application trend analysis
Geospatial data is an indispensable data resource for research and applications in many
fields. The technologies and applications related to geospatial data are constantly …
fields. The technologies and applications related to geospatial data are constantly …