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.
With pip:
pip install role-pattern-nlp
# 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.
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.
- SpaCy - DependencyMatcher
- SpaCy pattern builder
- networkx - Used by SpaCy pattern builder
