Results 11 to 20 of about 2,925,546 (286)
Unsupervised learning of generative topic saliency for person re-identification [PDF]
, 2014 (c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors.
Wang, H, Xiang, T, Gong, S
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Introduction: Is it a One Big Habitus? [PDF]
, 2022 This chapter presents an introductory study of the book. The chapter offers a brief introduction to the existing literature on women and leadership in public relations, the rationale for the book and then an ‘empirical’ reading of chapters.
Topic, M
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CoRR, 2021 We propose an Exclusive Topic Modeling (ETM) for unsupervised text classification, which is able to 1) identify the field-specific keywords though less frequently appeared and 2) deliver well-structured topics with exclusive words. In particular, a weighted Lasso penalty is imposed to reduce the dominance of the frequently appearing yet less relevant ...
Hao Lei, Ying Chen
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Enhancing Big Social Media Data Quality for Use in Short-Text Topic Modeling
IEEE Access, 2022 With the emergence of microblogging platforms and social media applications, large amounts of user-generated data in the form of comments, reviews, and brief text messages are produced every day.
Belal Abdullah Hezam Murshed +5 more
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Topic Models with Topic Ordering Regularities for Topic Segmentation [PDF]
2014 IEEE International Conference on Data Mining, 2014 Documents from the same domain usually discuss similar topics in a similar order. In this paper we present new ordering-based topic models that use generalised Mallows models to capture this regularity to constrain topic assignments. Specifically, these new models assume that there is a canonical topic ordering shared amongst documents from the same ...
Lan Du 0002 +2 more
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Conceptualization Topic Modeling [PDF]
CoRR, 2017 Recently, topic modeling has been widely used to discover the abstract topics in text corpora. Most of the existing topic models are based on the assumption of three-layer hierarchical Bayesian structure, i.e. each document is modeled as a probability distribution over topics, and each topic is a probability distribution over words.
Yi-Kun Tang +3 more
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Proceedings of the International AAAI Conference on Web and Social Media, 2021 Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method that learns the underlying themes in a large collection of otherwise unorganized documents.
Allison June-Barlow Chaney +1 more
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Enhancing topic clustering for Arabic security news based on k‐means and topic modelling
IET Networks, 2021 The internet has become one of the main sources of news spread as it unleashed the information dissemination space, where the news websites express opinions on entities while also reporting on recent or unusual security risks.
Adel R. Alharbi +2 more
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, 2012 We introduce the topical relevance model (TRLM) as a generalization of the standard relevance model (RLM). The TRLM alleviates the limitations of the RLM by exploiting the multi-topical structure of pseudo-relevant documents. In TRLM, intra-topical document and query term co-occurrences are favoured, whereas the inter-topical ones are down-weighted ...
Ganguly, Debasis +2 more
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Technology Project Summaries as a Predictor of Crowdfunding Success
Technology Innovation Management Review, 2022 Crowdfunding has emerged in recent years as an important alternative means for technology entrepreneurs to raise funds for their products and business ideas. While the success rate of crowdfunding projects is somewhat low, scholarly understanding of what
M. Westerlund +3 more
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