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The process takes all the available tokens (usually already tokenized by the "WhitespaceTokenizerStage") for the stage (using the highest confidence route), flags like "STOP_WORD" or "ALL_UPPER_CASE" can be used as filters by including them in the "Skip Flags" list.

Operates On:  Lexical Items with TOKEN and possibly other flags as specified below.

Configuration Parameters

• Parameter
  summary Index used by the stage to store dictionary data. default spellcheck_dictionary name index

  
  
  • This is an Elasticsearch index.

• Parameter
  summary Schema used by Elasticsearch connection default http name schema

• Parameter
  summary Hostname default localhost name host

• Parameter
  summary Port default 9200 name port type integer

"index": "saga_spellchecker_dictionary",
"schema": "http",
"host": "localhost",
"port": "9200"

Example Output

V--------------[abraham lincoln likes makaroni and cheese]--------------------V
^--[abraham]--V--[lincoln]--V--[likes]--V--[makaroni]--V--[and]--V--[cheese]--^
                                        ^--[macaroni]--^

Output Flags

Lex-Item Flags:

• MISSPELL- Identifies a token as potential misspelling.
• SUGGESTION - Added to the newly created token to identify it as a generated token and coming from the dictionary.

Vertex Flags:

Resource Data

The data used by the dictionary may come from 2 sources:

• Dataset
• Plain text file

Both options are accessed through Saga Server or the endpoints of the service.  To create a dictionary from a dataset, select the one you are interested in and select the pipeline to process it, remember that the pipeline must end with a Spellchecker Stage.  To create a dictionary from a file you only need a plain text file with terms separated by new line.

Resource Format

abraham
lincoln
likes
macaroni
and
cheese