Token and Phrase Spell Correction Job

Detect misspellings in queries or documents using the numbers of occurrences of words and phrases. This job extracts tail tokens (one word) and phrases (two words) and finds similarly-spelled head tokens and phrases. For example, if two queries are spelled similarly, but one leads to a lot of traffic (head) and the other leads to a little or zero traffic (tail), then it’s likely that the tail query is misspelled and the head query is its correction.

If several matching head tokens are found for each tail token, the job can pick the best correction using multiple configurable criteria.

1. Create a job

Create a Token and Phrase Spell Correction job in the Jobs Manager.

To create a new job
  1. In the Fusion workspace, navigate to Collections > Jobs.

  2. Click Add and select the job type Token and phrase spell correction.

    The New Job Configuration panel appears.

  3. Configure the new job as needed. Configuration is explained in the next section.

2. Configure the job

Configure the Token and Phrase Spell Correction job.

Required configuration

The configuration must specify:

  • The input collection (the Input Collection/trainingCollection parameter)

    The input collection can contain signal data or non-signal data. If it is signal data, then select Input is Signal Data (signalDataIndicator). Signals can be raw (from the _signals collection) or aggregated (from the _signals_aggr collection).

  • The input field (the Input Field/fieldToVectorize parameter)

  • The event count field

    For example, if signal data follows the default Fusion setup, then count_i is the field that records the count of raw signals and aggr_count_i is the field that records the count after aggregation.

Event types

The spell correction job lets you analyze query performance based on two different events:

  • The main event (the Main Event Type/mainType parameter)

  • The filtering/secondary event (the Filtering Event Type/filterType parameter)

    If you only have one event type, leave this parameter empty.

For example, if you specify the main event type to be click with a minimum count of 0 and the filtering event type to be query with a minimum count of 20, then the job will filter on the queries that get searched at least 20 times and check among those popular queries to see which ones didn’t get clicked at all, or were only clicked a few times.

Spell check documents

If you unselect the Input is Signal Data checkbox to indicate finding misspellings from content documents rather than signals, then you don’t need to specify the following parameters: Count Field, Main Event Field, Filtering Event Type, Field Name of Signal Type, Minimum Main Event Count and Minimum Filtering Event Count.

Use a custom dictionary

You can upload a custom dictionary of terms that are specific to your data, and specify it using the Dictionary Collection (dictionaryCollection) and Dictionary Field (dictionaryField) parameters. For example, in an e-commerce use case, you can use the catalog terms as the custom dictionary by specifying the product catalog collection as the dictionary collection and the product description field as the dictionary field.

Example configuration

This is an example configuration:

Spell correction job configuration

When you have configured the job, click Save to save the configuratiion.

3. Run the job

Run the Token and Phrase Spell Correction job.

Tip
If you are finding spelling corrections in aggregated data, you need to run an aggregation job before running the Token and Phrase Spelling Correction job. You don’t need to run a Head/Tail Analysis job. The Token and Phrase Spell Correction job does the head/tail processing it requires.
To run the job
  1. In the Fusion workspace, navigate to Collections > Jobs.

  2. Select the job from the job list.

  3. Click Run.

  4. Click Start.

4. Analyze job output

After the job finishes, misspellings and corrections are output into the output collection. An example record is as follows:

correction_s                  laptop battery
mis_string_len_i              14
misspelling_s                 laptop baytery
aggr_job_id_s                 162fcf94b20T3704c333
score                         1
collation_check_s             token correction included
corCount_misCount_ratio_d     2095
sound_match_b                 true
id                            bf79c43b-fc6d-43a7-931e-185fdac5b624
aggr_type_s                   tokenPhraseSpellCorrection
aggr_id_s                     ecom_spell_check
correction_types_s            phrase => phrase
cor_count_i                   68648960
suggested_correction_s        baytery=>battery
cor_string_len_i              14
token_wise_correction_s       baytery=>battery
cor_token_size_i              2
edit_dist_i                   1
timestamp_tdt                 2018-04-25T13:23:40.728Z
mis_count_i                   32768
lastChar_match_b              true
mis_token_size_i              2
token_corr_for_phrase_cnt_i   1

For easy evaluation, you can export the result output to a CSV file.

Spellcheck output

5. Use spell correction results

You can use the resulting corrections in various ways. For example:

  • Put misspellings into the synonym list to perform auto-correction.

  • Help evaluate and guide the Solr spellcheck configuration.

  • Put misspellings into typeahead or autosuggest lists.

  • Perform document cleansing (for example, clean a product catalog or medical records) by mapping misspellings to corrections.

Useful output fields

In the job output, you generally only need to analyze the suggested_corrections field, which provides suggestions about using token correction or whole-phrase correction. If the confidence of the correction is not high, then the job labels the pair as "review" in this field. Pay special attention to the output records with the "review" labels.

With the output in a CSV file, you can sort by mis_string_len (descending) and edit_dist (ascending) to position more probable corrections at the top. You can also sort by the ratio of correction traffic over misspelling traffic (the corCount_misCount_ratio field) to only keep high-traffic boosting corrections.

For phrase misspellings, the misspelled tokens are separated out and put in the token_wise_correction field. If the associated token correction is already included in the one-word correction list, then the collation_check field is labeled as "token correction include." You can choose to drop those phrase misspellings to reduce duplications.

Fusion counts how many phrase corrections can be solved by the same token correction and puts the number into the token_corr_for_phrase_cnt field. For example, if both "outdoor servailance" and "servailance camera" can be solved by correcting "servailance" to "surveillance", then this number is 2, which provides some confidence for dropping such phrase corrections and further confirms that correcting "servailance" to "surveillance" is legitimate.

You might also see cases where the token-wise correction is not included in the list. For example, "xbow" to "xbox" is not included in the list because it can be dangerous to allow an edit distance of 1 in a word of length 4. But if multiple phrase corrections can be made by changing this token, then you can add this token correction to the list.

Tip
Phrase corrections with a value of 1 for token_corr_for_phrase_cnt and with collation_check labeled as "token correction not included" could be potentially-problematic corrections.

Fusion labels misspellings due to misplaced whitespaces with "combine/break words" in the correction_types field. If there is a user-provided dictionary to check against, and both spellings are in the dictionary with and without whitespace in the middle, we can treat these pairs as bi-directional synonyms ("combine/break words (bi-direction)" in the correction_types field).

The sound_match and lastChar_match fields also provide useful information.