This job is deprecated in Fusion 5.9.15 and will be removed in a future release.
Lucidworks recommends migrating to Neural Hybrid Search, which achieves superior relevance compared to legacy machine learning methods.
Raw signals (the COLLECTION_NAME_signals collection by default)
Output
Extracted phrases (the COLLECTION_NAME_query_rewrite_staging collection by default)
query
count_i
type
timestamp_tdt
user_id
doc_id
session_id
fusion_query_id
Required signals fields:
✅
✅
✅
This job writes to the COLLECTION_NAME_query_rewrite_staging collection. It also uses reviewed documents from that collection to improve the accuracy of the job. You can review, edit, deploy, or delete output from this job using the Query Rewriting.Fusion ships with the OpenNLP Maxent model already loaded in the blob store.This job’s output, and output from the Token and Phrase Spell Correction job, can be used as input for the Synonym Detection job.
For most use cases, the minimum configuration for this job consists of these fields:
id/Spark Job ID
Give this job an arbitrary ID string.
trainingCollection/Training Collection
Specify the input collection.
fieldToVectorize/Field to Vectorize
Specify the field in the input collection where phrases can be found.
outputCollection/Output Collection
Specify the collection in which the output documents should be indexed.
When running this job over a content document collection, be sure to set attachPhrases/Extract Key Phrases from Input Text to “true”. The default is “false”, which works well when running the job over a signals collection.
By default, the job only outputs the phrases found from the original document. In each row of the phrases output, these fields are most useful:
The phrase itself is in the phrases_s field, which can be used for faceting.
The likelihood_d field gives the likelihood that the phrase is legitimate, from 0 to infinity.
Low-probability phrases are automatically trimmed from the results.
When a phrase’s likelihood value is ambiguous, the review field is set to “true” to indicate that the phrase should be reviewed.
A phrase_count field indicates the number of instances of the phrase in the input collection.
The complete list of output fields is shown below.
The name of the Phrase Extraction job that generated this document.
doc_type_s
This is always key_phrases for documents generated by a Phrase Extraction job.
id
A unique ID for this document.
input_collection
The collection used for this job’s input.
likelihood_d
The likelihood that this phrases_s is a phrase, from 0 to infinity.
phrase_count
The number of occurrences of this phrase in the input collection.
phrases_s
The phrase detected by the job.
review
”True” indicates that this may not be a valid phrase and should be reviewed.
score
This is always “1”.
timestamp
The date and time when the document was generated.
word_num_i
The number of words in this phrase.
_version_
An internal Solr field used for partial updates.
If the attachPhrases/Extract Key Phrases from Input Text parameter is set to “true”, then the job also outputs the original documents from the input collection with an appended field, phrases_extracted_tt, that lists the extracted phrases from this document.The way to distinguish the phrases output from the original document output is by the field doc_type_s, with one of these values:
key_phrases denotes phrases output.
original_doc_with_phrases denotes the original documents.
