Locality sensitive hashing (LSH) discovers potential matches among a corpus of documents quickly, so that only likely pairs can be compared.
The number of bands to use for locality sensitive hashing. The
number of hashes in the documents in the corpus must be evenly divisible by
the number of bands. See
Display a progress bar while comparing documents.
Locality sensitive hashing is a technique for detecting document
similarity that does not require pairwise comparisons. When comparing pairs
of documents, the number of pairs grows rapidly, so that only the smallest
corpora can be compared pairwise in a reasonable amount of computation time.
Locality sensitive hashing, on the other hand, takes a document which has
been tokenized and hashed using a minhash algorithm. (See
minhash_generator.) Each set of minhash signatures is then
broken into bands comprised of a certain number of rows. (For example, 200
minhash signatures might be broken down into 20 bands each containing 10
rows.) Each band is then hashed to a bucket. Documents with identical rows
in a band will be hashed to the same bucket. The likelihood that a document
will be marked as a potential duplicate is proportional to the number of
bands and inversely proportional to the number of rows in each band.
This function returns a data frame with the additional class
lsh_buckets. The LSH technique only requires that the signatures for
each document be calculated once. So it is possible, as long as one uses the
same minhash function and the same number of bands, to combine the outputs
from this function at different times. The output can thus be treated as a
kind of cache of LSH signatures.
To extract pairs of documents from the output of this function, see
A data frame (with the additional class
containing a column with the document IDs and a column with their LSH
signatures, or buckets.
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