Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering

@article{Jiang2021FamiliaAC,
  title={Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering},
  author={Di Jiang and Yuanfeng Song and Rongzhong Lian and Siqi Bao and Jinhua Peng and H. He and Hua Wu},
  journal={ArXiv},
  year={2021},
  volume={abs/1808.03733}
}
In the last decade, a variety of topic models have been proposed for text engineering. However, except Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA), most of existing topic models are seldom applied or considered in industrial scenarios. This phenomenon is caused by the fact that there are very few convenient tools to support these topic models so far. Intimidated by the demanding expertise and labor of designing and implementing parameter inference… 

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