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Unified Neural Topic Model via Contrasting Learning and Term Weighting Sung won Han1Mingi Shin1 Sunday Park2 Handbook Jung1 Meaning Cha3,1 1 School of Computing, WAIST, South Korea 2 Department of
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How to fill out unified neural topic model
How to fill out unified neural topic model
01
Prepare the dataset with a set of documents you want to analyze.
02
Preprocess the text data by removing stopwords, punctuation, and special characters.
03
Tokenize the text data into words or phrases.
04
Convert the text data into a numerical format using techniques like TF-IDF or word embeddings.
05
Train the unified neural topic model on the preprocessed and converted text data.
06
Evaluate the model performance using metrics like coherence score or perplexity.
07
Interpret the generated topics and analyze the results.
Who needs unified neural topic model?
01
Researchers in the field of natural language processing who want to discover latent topics in a large corpus of text data.
02
Businesses looking to extract valuable insights from customer reviews, social media data, or other text sources.
03
Academic institutions interested in analyzing trends and patterns in academic papers or research articles.
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