HiTR: Hierarchical Topic Model Re-Estimation for Measuring Topical Diversity of Documents

@article{Azarbonyad2019HiTRHT,
  title={HiTR: Hierarchical Topic Model Re-Estimation for Measuring Topical Diversity of Documents},
  author={Hosein Azarbonyad and Mostafa Dehghani and Tom Kenter and Maarten Marx and J. Kamps and Maarten de Rijke},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2019},
  volume={31},
  pages={2124-2137}
}
A high degree of topical diversity is often considered to be an important characteristic of interesting text documents. A recent proposal for measuring topical diversity identifies three distributions for assessing the diversity of documents: distributions of words within documents, words within topics, and topics within documents. Topic models play a central role in this approach and, hence, their quality is crucial to the efficacy of measuring topical diversity. The quality of topic models is… 
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