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d# 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.

Installation

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 = role_pattern_builder.build(match_example)  

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

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

API

RolePattern

RolePattern.spacy_dep_pattern

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

RolePattern.token_labels

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

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Build and match patterns for semantic role labelling / information extraction with SpaCy

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