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Spacy Regex Phrase Dependency Matcher in Python

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by vigneshchennai74 dot icon Updated: Jan 31, 2023

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The Dependency Matcher, a potent tool offered by the SpaCy library, can be used to match particular phrases based on the dependency parse of a sentence. Instead of matching word sequences based on their straightforward surface forms, the Dependency Matcher enables you to do so.  


The SpaCy Dependency Matcher can be used in a variety of ways to match particular phrases based on their dependencies, such as:  

  • Text categorization: You can extract particular phrases from text using the Dependency Matcher.  
  • Information extraction: The Dependency Matcher can be used to extract specific data from language, including attributes, costs, and features of goods.  
  • Question answering: The Dependency Matcher can be used to identify the subject, verb, and object in a sentence to improve the accuracy of question answering systems.  
  • Text generation: By matching particular phrases based on their dependencies, the Dependency Matcher may produce text that is grammatically accurate and semantically relevant.  
  • Text summarization: The Dependency Matcher can be used to identify key phrases that capture the text's essential concepts and serve as a summary.  

  

Here is how you can find the Spacy Regex Pharse Dependency Matcher in python

Preview of the output that you will get on running this code from your IDE

Code

In this solution we have used Pandas Library.

Spacy Regex Phrase Matcher in Python

PythondotLines of Code : 37dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import spacy
nlp = spacy.load('en_core_web_sm')
from spacy.matcher import Matcher
matcher = Matcher(nlp.vocab)

doc = nlp("""
Graham Greene is his favorite author. He wrote his first book when he was a hundred and fifty years old.
While writing this book, he had to fend off aliens and dinosaurs. Greene's second book might not have been written by him. 
Greene's cat in its deathbed testimony alleged that it was the original writer of the book. The fact that plot of the book revolves around 
rats conquering the world, lends credence to the idea that only a cat could have been the true writer of such an inane book.""")

matcher = Matcher(nlp.vocab)
pattern = [{"LEMMA": "write"},{"OP": "*"},{"LEMMA": "book"}]
matcher.add("testy", [pattern])

print("----- Using Matcher -----")
for sent in doc.sents:
    if matcher(sent):
        print(sent.text)

print("----- Using Dependency Matcher -----")

deppattern = [
        {"RIGHT_ID": "wrote", "RIGHT_ATTRS": {"LEMMA": "write"}},
        {"LEFT_ID": "wrote", "REL_OP": ">", "RIGHT_ID": "book", 
            "RIGHT_ATTRS": {"LEMMA": "book"}}
        ]

from spacy.matcher import DependencyMatcher

dmatcher = DependencyMatcher(nlp.vocab)

dmatcher.add("BOOK", [deppattern])

for _, (start, end) in dmatcher(doc):
    print(doc[start].sent)
  1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
  2. Enter the Text
  3. Run the code to get the Output


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for "Spacy Regex Phrase using Dependency matcher" in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.


  1. The solution is created in Python 3.7.15 Version
  2. The solution is tested on Spacy 3.4.3 Version


Using this solution, we can collect the sentence that user needs with the help of function in spacy . This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us extract the sentence in python.

Dependent Library

spaCyby explosion

Python doticonstar image 26383 doticonVersion:v3.2.6doticon
License: Permissive (MIT)

💫 Industrial-strength Natural Language Processing (NLP) in Python

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            spaCyby explosion

            Python doticon star image 26383 doticonVersion:v3.2.6doticon License: Permissive (MIT)

            💫 Industrial-strength Natural Language Processing (NLP) in Python
            Support
              Quality
                Security
                  License
                    Reuse

                      If you do not have SpaCy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the Spacy page in kandi.

                      You can search for any dependent library on kandi like SpaCy

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