Extracting Actionable Insights from Text Data: A Stable Topic Model Approach.
Y Yang, R Subramanyam - MIS Quarterly, 2023 - search.ebscohost.com
Topic models are becoming a frequently employed tool in the empirical methods repertoire
of information systems and management scholars. Given textual corpora, such as consumer …
of information systems and management scholars. Given textual corpora, such as consumer …
Nettaxo: Automated topic taxonomy construction from text-rich network
The automated construction of topic taxonomies can benefit numerous applications,
including web search, recommendation, and knowledge discovery. One of the major …
including web search, recommendation, and knowledge discovery. One of the major …
Taxogen: Unsupervised topic taxonomy construction by adaptive term embedding and clustering
Taxonomy construction is not only a fundamental task for semantic analysis of text corpora,
but also an important step for applications such as information filtering, recommendation …
but also an important step for applications such as information filtering, recommendation …
Exploring sequence-to-sequence taxonomy expansion via language model probing
Taxonomy is a knowledge graph of concept hierarchy which plays a significant role in
semantic entailment and is widely used in many downstream natural language processing …
semantic entailment and is widely used in many downstream natural language processing …
Steam: Self-supervised taxonomy expansion with mini-paths
Taxonomies are important knowledge ontologies that underpin numerous applications on a
daily basis, but many taxonomies used in practice suffer from the low coverage issue. We …
daily basis, but many taxonomies used in practice suffer from the low coverage issue. We …
Goal-driven explainable clustering via language descriptions
Unsupervised clustering is widely used to explore large corpora, but existing formulations
neither consider the users' goals nor explain clusters' meanings. We propose a new task …
neither consider the users' goals nor explain clusters' meanings. We propose a new task …
Taxocom: Topic taxonomy completion with hierarchical discovery of novel topic clusters
Topic taxonomies, which represent the latent topic (or category) structure of document
collections, provide valuable knowledge of contents in many applications such as web …
collections, provide valuable knowledge of contents in many applications such as web …
Co-embedding network nodes and hierarchical labels with taxonomy based generative adversarial networks
Network embedding aims at transferring node proximity in networks into distributed vectors,
which can be leveraged in various downstream applications. Recent research has shown …
which can be leveraged in various downstream applications. Recent research has shown …
[PDF][PDF] Efficient methods for incorporating knowledge into topic models
Latent Dirichlet allocation (LDA) is a popular topic modeling technique for exploring hidden
topics in text corpora. Increasingly, topic modeling needs to scale to larger topic spaces and …
topics in text corpora. Increasingly, topic modeling needs to scale to larger topic spaces and …
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 …