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• W2798720361 abstract "News related content has been extensively studied in both topic modeling research and named entity recognition. However, expressive power of named entities and their potential for improving the quality of discovered topics has not received much attention. In this paper we use named entities as domain-specific terms for news-centric content and present a new weighting model for Latent Dirichlet Allocation. Our experimental results indicate that involving more named entities in topic descriptors positively influences the overall quality of topics, improving their interpretability, specificity and diversity." @default.
• W2798720361 created "2018-05-07" @default.
• W2798720361 creator A5052326684 @default.
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• W2798720361 date "2018-01-01" @default.
• W2798720361 modified "2023-10-02" @default.
• W2798720361 title "Improving Topic Quality by Promoting Named Entities in Topic Modeling" @default.
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• W2798720361 doi "https://doi.org/10.18653/v1/p18-2040" @default.
• W2798720361 hasPublicationYear "2018" @default.
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