Topic Modeling with Document Relative Similarities
Topic modeling has been widely used in text mining. Previous topic models such as Latent Dirichlet Allocation (LDA) are successful in learning hidden topics but they do not take into account metadata of documents. To tackle this problem, many augmented topic models have been proposed to jointly mode...
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sg-smu-ink.sis_research-40702016-02-05T06:30:05Z Topic Modeling with Document Relative Similarities DU, Jianguang Jing JIANG, SONG, Dandan LIAO, Lejian Topic modeling has been widely used in text mining. Previous topic models such as Latent Dirichlet Allocation (LDA) are successful in learning hidden topics but they do not take into account metadata of documents. To tackle this problem, many augmented topic models have been proposed to jointly model text and metadata. But most existing models handle only categorical and numerical types of metadata. We identify another type of metadata that can be more natural to obtain in some scenarios. These are relative similarities among documents. In this paper, we propose a general model that links LDA with constraints derived from document relative similarities. Specifically, in our model, the constraints act as a regularizer of the log likelihood of LDA. We fit the proposed model using Gibbs-EM. Experiments with two real world datasets show that our model is able to learn meaningful topics. The results also show that our model outperforms the baselines in terms of topic coherence and a document classification task. 2015-07-31T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3070 https://ink.library.smu.edu.sg/context/sis_research/article/4070/viewcontent/P_ID_52343_IJCAI15_488_TopicModelingDocRelSimilarities.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Computer Sciences Databases and Information Systems |
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Computer Sciences Databases and Information Systems DU, Jianguang Jing JIANG, SONG, Dandan LIAO, Lejian Topic Modeling with Document Relative Similarities |
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Topic modeling has been widely used in text mining. Previous topic models such as Latent Dirichlet Allocation (LDA) are successful in learning hidden topics but they do not take into account metadata of documents. To tackle this problem, many augmented topic models have been proposed to jointly model text and metadata. But most existing models handle only categorical and numerical types of metadata. We identify another type of metadata that can be more natural to obtain in some scenarios. These are relative similarities among documents. In this paper, we propose a general model that links LDA with constraints derived from document relative similarities. Specifically, in our model, the constraints act as a regularizer of the log likelihood of LDA. We fit the proposed model using Gibbs-EM. Experiments with two real world datasets show that our model is able to learn meaningful topics. The results also show that our model outperforms the baselines in terms of topic coherence and a document classification task. |
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DU, Jianguang Jing JIANG, SONG, Dandan LIAO, Lejian |
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DU, Jianguang Jing JIANG, SONG, Dandan LIAO, Lejian |
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DU, Jianguang |
title |
Topic Modeling with Document Relative Similarities |
title_short |
Topic Modeling with Document Relative Similarities |
title_full |
Topic Modeling with Document Relative Similarities |
title_fullStr |
Topic Modeling with Document Relative Similarities |
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Topic Modeling with Document Relative Similarities |
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topic modeling with document relative similarities |
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Institutional Knowledge at Singapore Management University |
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2015 |
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https://ink.library.smu.edu.sg/sis_research/3070 https://ink.library.smu.edu.sg/context/sis_research/article/4070/viewcontent/P_ID_52343_IJCAI15_488_TopicModelingDocRelSimilarities.pdf |
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