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Build and match patterns for semantic role labelling

Project description

Role Pattern

Build and match linguistic patterns for role labelling. Provides an example-driven approach to generate and refine patterns.

Uses graph-based pattern matching, built on SpaCy.


With pip:

pip install role-pattern-nlp

Example usage

# First, parse a string to create a SpaCy Doc object
import en_core_web_sm
text = "Forging involves the shaping of metal using localized compressive forces."
nlp = en_core_web_sm.load()
doc = nlp(text)

from role_pattern_nlp import RolePatternBuilder

# Provide an example by mapping role labels to tokens
match_example = {
    'arg1': [doc[0]],  # [Forging]
    'pred': [doc[1]],  # [involves]
    'arg2': [doc[3]],  # [shaping]

''' Create a dictionary of all the features we want the RolePatternBuilder to have access to
when building and refining patterns '''
feature_dict = {'DEP': 'dep_', 'TAG': 'tag_'}

# Instantiate the pattern builder
role_pattern_builder = RolePatternBuilder(feature_dict)

#  Build a pattern. It will use all the features in the feature_dict by default
role_pattern =  

# Match against any doc with the role_pattern
matches = role_pattern.match(doc)
[{'arg1': [Forging], 'arg2': [shaping], 'pred': [involves]}]

See examples/ for demonstration as to how to refine a pattern using negative examples.




The dependency pattern in the form used to create the SpaCy DependencyMatcher object.


The list of labels that corresponds to the tokens matched by the pattern.

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