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Answer: Textual Indexing
**Textual Indexing** is the correct answer. In the context of a distributed computing environment, efficiently managing unstructured textual data for machine learning requires a method that allows for quick indexing and retrieval. Textual Indexing accomplishes this by creating a data structure that maps terms or phrases to their locations within the text, enabling rapid access to relevant information during analysis. This technique, which includes the use of inverted indexes, significantly enhances the speed and efficiency of text processing tasks by allowing for the quick lookup of terms and their occurrences across distributed datasets. While Textual Clustering, Partitioning, and Compression are valuable in various data processing scenarios, Textual Indexing specifically meets the need for efficient data retrieval in machine learning applications.
Author: LeetQuiz Editorial Team
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Your team is developing a machine learning model that needs to process unstructured textual data in a distributed computing environment. Which method is most effective for indexing and retrieving textual data efficiently for analysis?
A
Textual Partitioning
B
Textual Indexing
C
Textual Clustering
D
Textual Compression
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