Assessment of the Quality of Topic Models for Information Retrieval Applications
Author:
Affiliation:
1. The University of Melbourne, Parkville, VIC, Australia
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3578337.3605118
Reference41 articles.
1. Topic modeling algorithms and applications: A survey
2. N. Akhtar , M. M. S. Beg , and H. Javed . 2019. TextRank enhanced Topic Model for Query focussed Text Summarization . In Int. Conf. on Contemporary Computing. IEEE , Noida, India, 1--6. https://doi.org/10.1109/IC3. 2019 .8844939 10.1109/IC3.2019.8844939 N. Akhtar, M. M. S. Beg, and H. Javed. 2019. TextRank enhanced Topic Model for Query focussed Text Summarization. In Int. Conf. on Contemporary Computing. IEEE, Noida, India, 1--6. https://doi.org/10.1109/IC3.2019.8844939
3. LDA-AdaBoost.MH: Accelerated AdaBoost.MH based on latent Dirichlet allocation for text categorization
4. Task-Driven Comparison of Topic Models
5. M. Amami G. Pasi F. Stella and R. Faiz. 2016. An LDA-Based Approach to Scientific Paper Recommendation. In Natural Language Processing and Information Systems Elisabeth Métais Farid Meziane Mohamad Saraee Vijayan Sugumaran and Sunil Vadera (Eds.). Springer International Publishing Cham 200--210. https://doi.org/10.1007/978--3--319--41754--7_17 10.1007/978--3--319--41754--7_17
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