Extract synonyms, keywords from sentences using modified implementation of Aho Corasick algorithm
Flash Text <https://github.com/vi3k6i5/flashtext>
_.Synonym Extractor is a python library that is loosely based on Aho-Corasick algorithm <https://en.wikipedia.org/wiki/Aho%E2%80%93Corasick_algorithm>
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The idea is to extract words that we care about from a given sentence in one pass.
Basically say I have a vocabulary of 10K words and I want to get all the words from that set present in a sentence. A simple regex match will take a lot of time to loop over the 10K documents.
Hence we use a simpler yet much faster algorithm to get the desired result.
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pip install synonym-extractor
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# import module
from synonym.extractor import SynonymExtractor
# Create an object of SynonymExtractor
synonym_extractor = SynonymExtractor()
# add synonyms
synonym_names = ['NY', 'new-york', 'SF']
clean_names = ['new york', 'new york', 'san francisco']
for synonym_name, clean_name in zip(synonym_names, clean_names):
synonym_extractor.add_to_synonym(synonym_name, clean_name)
synonyms_found = synonym_extractor.get_synonyms_from_sentence('I love SF and NY. new-york is the best.')
synonyms_found
>> ['san francisco', 'new york', 'new york']
synonym-extractor is based on Aho-Corasick algorithm <https://en.wikipedia.org/wiki/Aho%E2%80%93Corasick_algorithm>
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Documentation can be found at Read the Docs <http://synonym-extractor.readthedocs.org>
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Say you have a corpus where similar words appear frequently.
eg: Last weekened I was in NY. I am traveling to new york next weekend.
If you train a word2vec model on this or do any sort of NLP it will treat NY and new york as 2 different words.
Instead if you create a synonym dictionary like:
eg: NY=>new york new york=>new york
Then you can extract NY and new york as the same text.
To do the same with regex it will take a lot of time:
============ ========== = ========= ============ Docs count # Synonyms : Regex synonym-extractor ============ ========== = ========= ============ 1.5 million 2K : 16 hours NA 2.5 million 10K : 15 days 15 mins ============ ========== = ========= ============
The idea for this library came from the following StackOverflow question <https://stackoverflow.com/questions/44178449/regex-replace-is-taking-time-for-millions-of-documents-how-to-make-it-faster>
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The project is licensed under the MIT license.