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Automatic Extraction of Useful Facet Hierarchies from Text Databases ISAM Hakka #1, Panagiotis G. Parents ?2 # Computer Science Department, Columbia University 1214 Amsterdam Avenue, New York, NY
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Automatic extraction of useful refers to the process of extracting valuable information or insights from a dataset using automated techniques such as data mining, machine learning algorithms, or natural language processing.
There is no specific requirement for individuals or organizations to file automatic extraction of useful. However, businesses in various industries often utilize automatic extraction techniques to gain useful insights for decision-making or to improve efficiency.
Automatic extraction of useful does not require a specific form or manual filing process. It typically involves implementing appropriate tools or technologies to automatically extract and analyze the desired information from datasets.
The purpose of automatic extraction of useful is to enable businesses and individuals to discover valuable insights, patterns, or relationships hidden within large datasets. This can facilitate data-driven decision-making, improve operational efficiency, or support various research and analysis tasks.
Automatic extraction of useful does not involve reporting of specific information. The focus is on extracting relevant and actionable insights from the dataset based on the objectives or requirements of the user.
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