What’s in a name? The effect of named entities on topic modelling interpretability

Author:

Tolochko Petro1,Balluff Paul1,Bernhard Jana1,Galyga Sebastian1,Lebernegg Noëlle S.1,Boomgaarden Hajo G.1ORCID

Affiliation:

1. Department of Communication, University of Vienna, Wien, Austria

Publisher

Informa UK Limited

Reference39 articles.

1. Bischof, J., & Airoldi, E. M. (2012). Summarizing topical content with word frequency and exclusivity. Proceedings of the 29th International Conference on Machine Learning (ICML-12), 201–208. Edinburgh Scotland.

2. A general pattern in the construction of economic newsworthiness? Analyzing news factors in popular, quality, regional, and financial newspapers

3. Stan: A Probabilistic Programming Language

4. Chang, J., & Blei, D. (2009). Relational topic models for document networks. In D. van Dyk & M. Welling (Eds.), Proceedings of the twelth international conference on artificial intelligence and statistics (pp. 81–88). Florida, USA.

5. Reading tea leaves: How humans interpret topic models;Chang J.;Advances in Neural Information Processing Systems,2009

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1. Bootstrapping public entities. Domain-specific NER for public speakers;Communication Methods and Measures;2024-08-13

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