phruzz-matcher 0.0.4

Combination of the RapidFuzz library with Spacy PhraseMatcher

phruzz-matcher

Combination of the RapidFuzz library with Spacy PhraseMatcher The goal of this component is to find matches when there were NO "perfect matches" due to typos or abbreviations between a Spacy doc and a list of phrases. To see more about Spacy Phrase Matcher go to https://spacy.io/usage/rule-based-matching#phrasematcher

Installation (dev)

    git clone https://github.com/mjvallone/phruzz_matcher_spacy.git

Configuration (dev)

1. Create virtualenv using python3 (follow https://virtualenvwrapper.readthedocs.io/en/latest/install.html)

    virtualenv venv
   

2. Activate the virtualenv

    . venv/bin/activate
   

3. Install requirements

    pip install -r requirements.txt
   

Usage

First you need to install it

pip install phruzz_matcher

If you want to add it to your pipeline you could do something like this:

from phruzz_matcher.phrase_matcher import PhruzzMatcher

@Language.factory("phrase_matcher")
def phrase_matcher(nlp: Language, name: str):
    return PhruzzMatcher(nlp, list_of_phrases, entity_label, match_percentage)


nlp.add_pipe("phrase_matcher")

Parameters

• nlp: the Spacy model you use (it was tested with the different Spanish models from Spacy).
• list_of_phrases: the list of phrases you want to find in the Spacy doc.
• entity_label: when finding matches you need to specify which entity label will replace them in the Spacy doc.
• match_percentage: percentage from the one you will keep matches between text from Spacy doc and the list of phrases. Higher the percentage, lower the differences "tolerated" to find a match.

Result

Based on Spacy documentation "A pipeline component is a function that receives a Doc object, modifies it and returns it", so the PhruzzMatcher returns a Doc object. For further information visit https://spacy.io/usage/processing-pipelines#custom-components

Example

import spacy
from spacy.language import Language
from phruzz_matcher.phrase_matcher import PhruzzMatcher

famous_people = [
        "Brad Pitt",
        "Demi Moore",
        "Bruce Willis",
        "Jim Carrey",
]

@Language.factory("phrase_matcher")
def phrase_matcher(nlp: Language, name: str):
    return PhruzzMatcher(nlp, famous_people, "FAMOUS_PEOPLE", 85)

nlp = spacy.blank("es")
nlp.add_pipe("phrase_matcher")

doc = nlp("El otro día fui a un bar donde vi a brad pit y a Demi Moore, estaban tomando unas cervezas mientras charlaban de sus asuntos.")
print(f"doc.ents: {doc.ents}")
#doc.ents: (brad pit, Demi Moore)