idiomatch

An implementation of SpaCy(3.0)'s Matcher specifically designed for identifying English idioms.

Install

pip3 install idiomatch  # install the library
python3 -m spacy download en_core_web_sm  # idiomatch depends on SpaCy's en_core_web_sm model

Dependencies

• spacy >= 3.0.1
• en-core-web-sm >= 3.1.0

Quick Start

import spacy
from idiomatch import Idiomatcher


def main():
    sent = "The floodgates will remain opened for a host of new lawsuits."  # a usecase of *open the floodgates*
    nlp = spacy.load("en_core_web_sm")  # idiom matcher needs an nlp pipeline; Currently supports en_core_web_sm only.
    idiomatcher = Idiomatcher.from_pretrained(nlp)  # this will take approx 50 seconds.
    doc = nlp(sent)  # process the sentence with an nlp pipeline
    print(idiomatcher.identify(doc))  # identify the idiom in the sentence


if __name__ == '__main__':
    main()

adding patterns into idiom_matcher...: 100%|██████████| 2756/2756 [00:52<00:00, 52.83it/s]
[{'idiom': 'open the floodgates', 'span': 'The floodgates will remain opened', 'meta': (13612509636477658373, 0, 5)}]

Supported Idioms

List of supported idioms can be found in idiomatch/resources/idioms.txt. Total of 2758 idioms are available for matching. These "target idioms" were extracted from a vocabulary of 5000 most frequently used English idioms, which had been made available for open use courtesy of IBM's SLIDE project.

Adding Idioms Yourself

If you have idioms that are not included in the list of supported idioms, you can add them to Idiomatcher yourself with the add_idioms member method:

import spacy
from idiomatch import Idiomatcher


def main():
    nlp = spacy.load("en_core_web_sm")
    idiomatcher = Idiomatcher(nlp)  # instantiate 
    # As for a placeholder for openslot, use either: someone / something / someone's / one's 
    idioms = ["have blood on one's hands", "on one's hands"]
    idiomatcher.add_idioms(idioms)  # this will train idiomatcher to identify the given idioms
    sent = "The leaders of this war have the blood of many thousands of people on their hands."
    doc = nlp(sent)
    print(idiomatcher.identify(doc))


if __name__ == '__main__':
    main()

100%|██████████| 2/2 [00:00<00:00, 145.62it/s]
adding patterns into idiom_matcher...: 100%|██████████| 2/2 [00:00<00:00, 196.40it/s]
[{'idiom': "have blood on one's hands", 'span': 'have the blood of many thousands of people on their hands', 'meta': (5930902300252675198, 5, 16)}, {'idiom': "on one's hands", 'span': 'on their hands', 'meta': (8246625119345375174, 13, 16)}]

Supported Variations

English idioms extensively vary in forms, at least in six different ways. Idiomatcher can gracefully handle all the cases, as exemplified below:

variation	example	result
modification	He called my blatant bluff	[{'idiom': "call someone's bluff", 'span': 'called my blatant bluff', 'meta': (11321959191976266509, 1, 5)}]
openslot	This will keep all of us posted	[{'idiom': 'keep someone posted', 'span': 'keep all of us posted', 'meta': (11722464987668971331, 2, 7)}]
hyphenated	That was one balls-out street race!	[{'idiom': 'balls-out', 'span': 'balls - out', 'meta': (2876800142358111704, 3, 6)}]
hyphen omitted	That was one balls out street race!	[{'idiom': 'balls-out', 'span': 'balls out', 'meta': (2876800142358111704, 3, 5)}]
passivisation (modification)	the floodgates are finally opened	[{'idiom': 'open the floodgates', 'span': 'the floodgates are finally opened', 'meta': (13612509636477658373, 0, 5)}]
passivisation (openslot)	my bluff was embarrassingly called by her	[{'idiom': "call someone's bluff", 'span': 'my bluff was embarrassingly called', 'meta': (11321959191976266509, 0, 5)}]
inclusion	If she dies, you wil have her blood on your hands!	[{'idiom': "have blood on one's hands", 'span': 'have her blood on your hands', 'meta': (5930902300252675198, 6, 12)}, {'idiom': "on one's hands", 'span': 'on your hands', 'meta': (8246625119345375174, 9, 12)}]

How Does it Work?

The idiom-matching patterns, which are the foundations of Idiomatcher's flexibility, are heavily inspired by Hughs et al.'s briliant work (2021) on Flexible Retrieval of Idiomatic Expressions from a Large Text Corpus.