Extract synonyms from sentences using Aho Corasick algorithm
Project description
synonym-extractor
==============
Synonym Extractor is a python library that based on `Aho-Corasick algorithm
<https://en.wikipedia.org/wiki/Aho%E2%80%93Corasick_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.
Installation
-------
::
pip install synonym-extractor
Usage
------
::
# import module
from synonym.extractor import SynonymExtractor
# Create an object of SynonymExtractor
synonym_extractor = SynonymExtractor()
# add synonyms to it
synonym_names = ['NY', 'SF']
clean_names = ['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')
synonyms_found
>> ['san francisco', 'new york']
Algorithm
----------
synonym-extractor is based on `Aho-Corasick algorithm
<https://en.wikipedia.org/wiki/Aho%E2%80%93Corasick_algorithm>`_.
Documentation
----------
Documentation can be found at `Read the Docs
<http://synonym-extractor.readthedocs.org>`_.
Why
------
::
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>`_.
License
-------
The project is licensed under the MIT license.
==============
Synonym Extractor is a python library that based on `Aho-Corasick algorithm
<https://en.wikipedia.org/wiki/Aho%E2%80%93Corasick_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.
Installation
-------
::
pip install synonym-extractor
Usage
------
::
# import module
from synonym.extractor import SynonymExtractor
# Create an object of SynonymExtractor
synonym_extractor = SynonymExtractor()
# add synonyms to it
synonym_names = ['NY', 'SF']
clean_names = ['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')
synonyms_found
>> ['san francisco', 'new york']
Algorithm
----------
synonym-extractor is based on `Aho-Corasick algorithm
<https://en.wikipedia.org/wiki/Aho%E2%80%93Corasick_algorithm>`_.
Documentation
----------
Documentation can be found at `Read the Docs
<http://synonym-extractor.readthedocs.org>`_.
Why
------
::
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>`_.
License
-------
The project is licensed under the MIT license.
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