Supervising topic models with Gaussian processes

Kandemir, Melih; Kekeç, Taygun; Yeniterzi, Reyyan

doi:10.1016/j.patcog.2017.12.019

Cited by 15 publications

(15 citation statements)

References 13 publications

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“…• GPSTM: Gaussian process supervised topic models [24] extend parametric supervised topic models [42] by using Bayesian GP-LVMs as classifiers instead of linear classifiers and the labels are conditioned on news stories' topics.…”

Section: Methodsmentioning

confidence: 99%

“…Bayesian GP-LVM extends GP-LVM [33,34] by incorporating additional priors and conducting posterior inference using variational methods [23,68]. Kandemir et al recently proposed GPSTM as a joint model between LDA and Bayesian GP-LVM and demonstrate the performance gain of jointly modeling LDA and GPLVM over linear classifiers [24]. Lastly, while our model focus on the joint modeling of text and users' sharing patterns [26], textual data has also been aligned with users' temporal traces to model diffusion pattern and text [16,21], to cluster the documents in both discrete [1] and continuous [15,69,41] time domains.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Homogeneity-Based Transmissive Process to Model True and False News in Social Networks

Kim

2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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An overwhelming number of true and false news stories are posted and shared in social networks, and users diffuse the stories based on multiple factors. Diffusion of news stories from one user to another depends not only on the stories' content and the genuineness but also on the alignment of the topical interests between the users. In this paper, we propose a novel Bayesian nonparametric model that incorporates homogeneity of news stories as the key component that regulates the topical similarity between the posting and sharing users' topical interests. Our model extends hierarchical Dirichlet process to model the topics of the news stories and incorporates Bayesian Gaussian process latent variable model to discover the homogeneity values. We train our model on a real-world social network dataset and find homogeneity values of news stories that strongly relate to their labels of genuineness and their contents. Finally, we show that the supervised version of our model predicts the labels of news stories better than the state-of-the-art neural network and Bayesian models.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Homogeneity-Based Transmissive Process to Model True and False News in Social Networks

Kim

2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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“…Kandemir et al [41] proposed a new way to supervise LDA with Gaussian processes as nonlinear predictors. Thus they can perform topic modeling and document classification jointly.…”

Section: Literature Reviewmentioning

confidence: 99%

NET-LDA: a novel topic modeling method based on semantic document similarity

Ekinci¹,

Omurca²

2020

Turk J Elec Eng & Comp Sci

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Topic models, such as latent Dirichlet allocation (LDA), allow us to categorize each document based on the topics. It builds a document as a mixture of topics and a topic is modeled as a probability distribution over words.However, the key drawback of the traditional topic model is that it cannot handle the semantic knowledge hidden in the documents. Therefore, semantically related, coherent and meaningful topics cannot be obtained. However, semantic inference plays a significant role in topic modeling as well as in other text mining tasks. In this paper, in order to tackle this problem, a novel NET-LDA model is proposed. In NET-LDA, semantically similar documents are merged to bring all semantically related words together and the obtained semantic similarity knowledge is incorporated into the model with a new adaptive semantic parameter. The motivation of the study is to reveal the impact of semantic knowledge in the topic model researches. Therefore, in a given corpus, different documents may contain different words but may speak about the same topic. For such documents to be correctly identified, the feature space of the documents must be elaborated with more powerful features. In order to accomplish this goal, the semantic space of documents is constructed with concepts and named entities. Two datasets in the English and Turkish languages and 12 different domains have been evaluated to show the independence of the model from both language and domain. The proposed NET-LDA, compared to the baselines, outperforms in terms of topic coherence, F-measure, and qualitative evaluation.

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“…It finds more meaningful contextual structure. Kandemir et al, [77] worked on by integrating LDA and sparse Gaussian processes. It classifies topic by joining latent cryptographic variables with each topic associated in the document.…”

Section: Topic Modeling On Short Textsmentioning

confidence: 99%

“…Google News - [22], [66], [80] Snippets 8 [8], [29], [36], [44], [45], [49][50][51][52], [68], [74], [75] NIPS - [38] DBLP 6 [31], [37], [62] Yahoo! Answers 11 [38], [45], [77], [82] Online News 7 [26][27][28][29], [31], [32], [37], [47], [62], [68], [81] Baidu Q & A 35 [49], [52], [37], [81][35] [15], [39], [75] Satck Overflow Q & A - [29], [44], [47] Tweets {August to Oct 2008}…”

Section: Short Textmentioning

confidence: 99%