Extraction of stable phrases (bigrams and trigrams), counters of occurrences of phrases/lemmas and individual words/lemmas in the corpus of books. Together with lemma combinations, inflectional forms corresponding to their word combinations are extracted, that is, each lemma combination corresponds to a set of word combinations in a natural way, along with their counteractions.

The main entry point is the extract.cpp file, whose functions are documented in the code.

./colloc_extract corpus_dir save_dir

corpus_dir contains compressed text files. The result is files in the output folder save_dir.
the main parameters are:

1. threshold by the number of participants in meetings of lemma combinations threshold (function group_lem2/3)\
2. the threshold th1 according to the composition of documents, containing the lemma combination and the probabilistic threshold th2, which determines whether the phrase is stable, which is calculated by the formula below (the filter_bilems/trilems function).

Sample output file for bigrams:

00f0a0bdb1 00efbba6 1384.324108337
                              burning bush 328
                              burning bushes 1
                              burning bush 5
                              burning bush 269
                              burning bush 925
                              burning bushes 19
                              burning bush 263
                              burning bush 1
                              burning bush 24
                              burning bush 1421

00f0a0bdb1 00efbba6 is a lemma combination, 1384.324108337 is a sign of the lemma combination stability (the higher, the more stable), 328 is the number of times the phrase unburnt kup occurs in all documents.

Accumulation calculations on a machine with NVMe disk and 16 GB RAM and in total it takes about 6 hours to get bigrams and trigrams together. An SSD drive is desirable, but will be significantly slower when converting words to identifiers (the convert function)
The formula for calculating the probability of a joint meeting of words in a phrase (bigram) is taken from the article https://arxiv.org/abs/1310.4546 paragraph 4. score = (Wij - threshold)/(Wi*Wj) The same formula is easily extended to the case trigram.
There is a toy example with a zero threshold in the example.txt file, which allows you to understand how everything works on the fingers.
The convert.pl script has functions that convert Libruks zip archives from fb2 to text files. The already converted files are in searchdev:/mnt/LibruksTxt, specifically in this folder related to corpus_dir: ./colloc_extract corpus_dir save_dir.
For fast serialization/deserialization on disk records, the capnp library is used, which is several times faster than protobuf. This is especially useful when iterating over a corpus that contains a large binary file.
There is also a gramcat utility for viewing binary files, which accepts several parameters. ``sh gramcat uni.bin |rg "^(and|also)\s+" gramcatlemid.bin | rg "^(00f0ad8192|00f0ad8cac)\s+" gramcat bifiltered.bin | pr "^4222130\s+6552893" gramcat bi.bin |rg "^22\s+8256\s+" gramcat bifiltered.bin uni.bin lemid.bin | rg "a\s+also"

where `rg` is `ripgrep`\
She, depending on the type of file, selects the function for parsing. The type of filtering at the beginning of the file itself.


# install capnp

``sh
curl -O https://capnproto.org/capnproto-c++-0.9.1.tar.gz
tar zxf capnproto-c++-0.9.1.tar.gz
cd capnproto-c++-0.9.1
./tune
do -j6 check
sudo do install