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Using spaCy Matcher to find patterns in text

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by vigneshchennai74 dot icon Updated: Feb 20, 2023

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This kit demonstrates how to use the spaCy Matcher object to find specific patterns in text. This can be useful in many natural language processing tasks, such as extracting specific phrases or entities from text, identifying specific language patterns or syntax, or detecting certain types of language use. 


By understanding how to use the Matcher object in spaCy, you can more easily extract relevant information from your natural language processing projects. This can be particularly useful in information extraction, sentiment analysis, and named entity recognition tasks. Additionally, understanding how to work with the spaCy library and its built-in language models can save you significant time and effort when working with natural language text in Python. 


The Matcher in spaCy is an object that allows you to find sequences of tokens in a text based on a pattern defined using spaCy's Doc object vocabulary. The Matcher can be useful in many natural language processing tasks where you need to find specific phrases or entities in text. 


The "en_core_web_sm" argument in this code is the language model spaCy uses to process the text. "en_core_web_sm" is the small English language model provided by spaCy, which is trained on web text and designed for small-to-medium-sized projects. You can also use other pre-trained models or train your models in spaCy. The language model is important because it provides the basis for the spaCy Doc object, which is used to create matches with the Matcher object. 


The spaCy Matcher object can be useful for many natural languages processing tasks, such as information extraction, named entity recognition, and sentiment analysis. It can also save significant time and effort when working with natural language text in Python. 

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

Code

In this solution we have used Matcher function from SpaCy

Tweek spacy spans

PythondotLines of Code : 30dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import spacy
from spacy.matcher import Matcher

nlp = spacy.load("en_core_web_sm")
matcher = Matcher(nlp.vocab)
# Add match ID "HelloWorld" with no callback and one pattern
pattern = [{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}]
matcher.add("HelloWorld", [pattern])

doc = nlp("Hello, world! Hello world!")
matches = matcher(doc)
for match_id, start, end in matches:
    string_id = nlp.vocab.strings[match_id]  # Get string representation
    span = doc[start:end]  # The matched span
    print(match_id, string_id, start, end, span.text)

import spacy
from spacy.matcher import Matcher

nlp = spacy.load('en_core_web_lg')
text='The car comprises 4 brakes 4.1, 4.2, 4.3 and 4.4 in fig. 5, all include an ESP system. This is shown in Fig. 6. Fig. 5 shows how the motors 56 and 57 are blocked. Besides the doors (44, 45) are painted blue.'

# Add EntityRuler to pipeline
ruler = nlp.add_pipe("entity_ruler", before="ner", config={"validate": True})
patterns = [{"label": "2_DIGIT", "pattern": [{"IS_DIGIT": True}, {"IS_PUNCT": True}, {"IS_DIGIT": True}]}]
ruler.add_patterns(patterns)

# Print 2-Digit Ents
print([(ent.label_, text[ent.start_char:ent.end_char]) for ent in doc.ents if ent.label_ == "2_DIGIT"])
  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 get the rules to execute


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 "Tweek Spacy Spans" 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 define the rules using spacing matcher with the help of spacy library in python . This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us matching based on the rules 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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