Results 41 to 50 of about 1,022,373 (272)
Efficient Correlated Topic Modeling with Topic Embedding [PDF]
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017 KDD 2017 oral.
Zhiting Hu +4 more
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IEEE Access, 2023 Topic modelling is important for tackling several data mining tasks in information retrieval. While seminal topic modelling techniques such as Latent Dirichlet Allocation (LDA) have been proposed, the ubiquity of social media and the brevity of its texts
Michael Bewong +7 more
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Effective Neural Topic Modeling with Embedding Clustering Regularization [PDF]
arXiv, 2023 Topic models have been prevalent for decades with various applications. However, existing topic models commonly suffer from the notorious topic collapsing: discovered topics semantically collapse towards each other, leading to highly repetitive topics, insufficient topic discovery, and damaged model interpretability.
arxiv
Topic Modeling in Embedding Spaces [PDF]
Transactions of the Association for Computational Linguistics, 2020 Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the embedded topic model (etm), a generative model of documents that marries traditional topic models with word embeddings.
Francisco J. R. Ruiz +2 more
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Information, 2019 Social networks like Twitter are increasingly important in the creation of new ways of communication. They have also become useful tools for social and linguistic research due to the massive amounts of public textual data available.
Joseba Fernandez de Landa +2 more
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Comparison of Topic Modelling Approaches in the Banking Context
Applied Sciences, 2023 Topic modelling is a prominent task for automatic topic extraction in many applications such as sentiment analysis and recommendation systems. The approach is vital for service industries to monitor their customer discussions.
Bayode Ogunleye +4 more
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Local and Global Topics in Text Modeling of Web Pages Nested in Web Sites [PDF]
arXiv, 2021 Topic models are popular models for analyzing a collection of text documents. The models assert that documents are distributions over latent topics and latent topics are distributions over words. A nested document collection is where documents are nested inside a higher order structure such as stories in a book, articles in a journal, or web pages in a
arxiv
Discriminative Relational Topic Models [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 Many scientific and engineering fields involve analyzing network data. For document networks, relational topic models (RTMs) provide a probabilistic generative process to describe both the link structure and document contents, and they have shown promise on predicting network structures and discovering latent topic representations.
Bo Zhang, Jun Zhu, Fei Xia, Ning Chen
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, 2013 In this paper we present a modification to a latent topic model, which makes the model exploit supervision to produce a factorized representation of the observed data. The structured parameterization separately encodes variance that is shared between classes from variance that is private to each class by the introduction of a new prior over the topic ...
Zhang, Cheng +3 more
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A Formal Model for Topic Maps [PDF]
, 2002 Topic Maps have been developed in order to represent the structures of relationships between subjects, independently of resources documenting them, and to allow standard representation and interoperability of such structures. The ISO 13250 XTM specification have provided a robust syntactic XML representation allowing processing and interchange of topic
Auillans, Pascal +3 more
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