1 views collected around this technical thread.
This article proposes a method to identify low-quality text in business data by training a large-scale unsupervised language model to compute sentence perplexity, converting the detection problem into a threshold decision, and details model design, challenges, optimizations, and online performance results.
This article explains the concepts, training pipeline, deployment formats, and practical applications of language models—particularly LSTM‑based models—for evaluating and generating text quality in a real‑world rental listing platform, highlighting data preparation, model training, and online serving techniques.
This article introduces the fundamentals of topic modeling, compares the probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation (LDA) methods, explains their graphical models and inference via EM or Gibbs sampling, and discusses practical strategies for selecting the optimal number of topics using perplexity or hierarchical Dirichlet processes.