Extract synonyms from sentences using Aho Corasick algorithm

algorithm, datastructures, nlp, python, synonyms
pip install synonym-extractor==1.0


This project has moved to Flash Text.


Synonym Extractor is a python library that is loosely based on Aho-Corasick algorithm.

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.


pip install synonym-extractor


# 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.')

>> ['san francisco', 'new york', 'new york']


synonym-extractor is based on Aho-Corasick algorithm.


Documentation can be found at Read the Docs.


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.


The project is licensed under the MIT license.