Using topic-noise models to generate domain-specific topics across data sources
Author:
Funder
National Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10115-022-01805-2.pdf
Reference40 articles.
1. Churchill R, Singh L (2020) Percolation-based topic modeling for tweets. In: WISDOM 2020: KDD workshop on issues of sentiment discovery and opinion mining
2. Churchill R, Singh L, Kirov C (2018) A temporal topic model for noisy mediums. In: pacific-asia conference on knowledge discovery and data mining (PAKDD)
3. Chemudugunta C, Smyth P, Steyvers M (2007) Modeling general and specific aspects of documents with a probabilistic topic model. In: Advances in neural information processing systems (NIPS)
4. Li C, Wang H, Zhang Z, Sun A, Ma Z (2016) Topic modeling for short texts with auxiliary word embeddings. In: ACM SIGIR conference on research and development in information retrieval, pp. 165–174
5. Churchill R, Singh L (2021) Topic-noise models: modeling topic and noise distributions in social media post collections. In: International conference on data mining (ICDM)
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