Spelling Corrector
Description	Spelling Corrector enables you to correct the most cumbersome mistakes, with a high degree of accuracy and speed.
Why to use	Textual Analysis – Pre Processing
When to use	When there are words present in the textual data that may have been mis-spelled.	When not to use	On numerical data.
Prerequisites	It is used on textual data.
Input	Computer	Output	Computer
Predecessor	Case Convertor Custom Words Remover Frequent Words Remover Lemmatizer Spelling Corrector Stemmer Advanced Entity Extraction Word Correlation Word Frequency	Successor	Case Convertor Custom Words Remover Frequent Words Remover Lemmatizer Spelling Corrector Stemmer Advanced Entity Extraction Word Correlation Word Frequency
Statistical Methods used	-	Limitations	Only the spellings of the words that are present in dictionary corrected.

Spelling Corrector is located under Textual Analysis () in Pre Processing, in the task pane on the left. Use drag-and-drop method to use algorithm in the canvas. Click the algorithm to view and select different properties for analysis.

Spelling corrector enables the user to correct the most cumbersome mistakes and ensures a higher degree of accuracy and speed.

Properties of Spelling Corrector

The available properties of Spelling Corrector are as shown in the figure given below.

The table given below describes different fields present on properties of Spelling Corrector.

Field		Description	Remark
Task Name		It displays the name of the selected task.	You can click the text field to edit or modify the name of the task as required.
Text		It allows you to select the text in which you want to correct the spellings.	Only one data field can be selected Textual data fields selected for the reader are visible. Only textual data field can be selected
Advanced	Node Configuration	It allows you to select the instance of the AWS server to provide control on the execution of a task in a workbook or workflow.	For more details, refer to Worker Node Configuration.

Interpretation of Spelling Corrector

The figure given below shows the result of Punctuation Remover applied on Google News snippets.
In the figure, the column heading CSCText represents the text after the Spelling Corrector is applied.
In the highlighted example, the word "clocked" has been replaced by the word "blocked".

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