PRF

Pseudo (or blind) Relevance-Feedback module. The Query-Augmentation method applies a fast document retrieving method and then extracts keywords from relevant documents. Thus, we have to retrieve top words from relevant documents to give a proper augmentation of a given query.

Parameters

• on (Union[str, list])

  Fields to use for fitting the spelling corrector on.

• documents (list)

• tf (sklearn.feature_extraction.text.CountVectorizer) – defaults to sparse.TfidfVectorizer()

  defaults to sklearn.feature_extraction.text.sparse.TfidfVectorizer. If you want to implement your own tf, it needs to follow the sklearn base API and provides the transform fit_transform and get_feature_names_out methods. See sklearn documentation for more information.

• nb_docs (int) – defaults to 5

  Number of documents from which to retrieve top-terms.

• nb_terms_per_doc (int) – defaults to 3

  Number of terms to extract from each top documents retrieved.

Attributes

• type

Examples

>>> from cherche import query, data

>>> documents = data.load_towns()

>>> prf = query.PRF(
...     on=["title", "article"],
...     nb_docs=8, nb_terms_per_doc=1,
...     documents=documents
... )

>>> prf
Query PRF
    on       : title, article
    documents: 8
    terms    : 1

>>> prf(q="Europe")
'Europe art metro space science bordeaux paris university significance'

>>> prf(q=["Europe", "Paris"])
['Europe art metro space science bordeaux paris university significance', 'Paris received paris club subway billion source tour tournament']

Methods

call

Augment a given query with new terms.

Parameters

• q (Union[List[str], str])
• kwargs

References

1. Relevance feedback and pseudo relevance feedback
2. Blind Feedback