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Part II: Multifaceted Taxonomy
Construction
ADD 2020 Tutorial
EmbeddingDriven Multidimensional Topic Mining and Text Analysis
You Men, Jain Huang, Diana Han
Computer Science, University of Illinois
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How to fill out embedding driven multi dimensional
01
Identify the dimensions of the data that need to be embedded.
02
Choose an appropriate embedding technique such as Word2Vec or GloVe.
03
Preprocess the data to convert it into the required input format for the embedding model.
04
Train the embedding model on the preprocessed data to learn the embeddings for each dimension.
05
Use the learned embeddings to represent the multi-dimensional data in a lower dimensional space.
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01
Researchers working on natural language processing tasks such as sentiment analysis or document classification.
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Data scientists looking to visualize high-dimensional data in a more interpretable manner.
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Machine learning engineers developing recommendation systems or personalized marketing strategies.
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What is embedding driven multi dimensional?
Embedding driven multi dimensional is a technique used to map high-dimensional data into lower-dimensional space.
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The purpose of embedding driven multi dimensional is to simplify and visualize complex data structures for easier analysis.
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