bagpipes-spacy 0.1.2

A collection of spaCy components for rules-based detection.

Bagpipes spaCy

Bagpipes spaCy is a collection of custom spaCy pipeline components designed to enhance text processing capabilities. These components include:

1. Quote Detector: Identifies and extracts quotes from the text.
2. Phrases Extractor: Extracts various types of phrases such as prepositional, noun, verb, adjective, and adverbial phrases.
3. Normalizer: Normalizes the text by expanding contractions, removing special characters, and more.
4. Triple Detector: Extracts triples (subject, predicate, object) from the text.
5. Entity Similarity: Computes similarity between entities in the text and maps similar entities.

Table of Contents

• Bagpipes spaCy
  • Table of Contents
  • Installation
  • Usage
    • Integrating the Components into your spaCy Pipeline
    • Text Processing with the Pipeline
      • Retrieving the Extracted Quotes
      • Retrieving the Extracted Phrases
      • Retrieving the Normalized Text
      • Retrieving the Extracted Triples
      • Retrieving the Entity Similarities and Mappings

Installation

To install Bagpipes spaCy, execute:

pip install bagpipes-spacy

Usage

Integrating the Components into your spaCy Pipeline

Begin by importing the components and then integrating them into your spaCy pipeline:

import spacy
from bagpipes_spacy import QuoteDetector, PhrasesExtractor, Normalizer, TripleDetector, EntitySimilarity

# Initialize your preferred spaCy model
nlp = spacy.blank('en')

# Integrate the components into the pipeline
nlp.add_pipe('quote_detector')
nlp.add_pipe('phrases_extractor')
nlp.add_pipe('normalizer')
nlp.add_pipe('triple_detector')
nlp.add_pipe('entity_similarity')

Text Processing with the Pipeline

After adding the components, you can process text as you typically would:

text = "She said, \"I'm going to the store.\" The store is located near the river."
doc = nlp(text)

Retrieving the Extracted Quotes

You can access the extracted quotes from the doc._.quotes attribute:

for quote in doc._.quotes:
    print(quote)

Output:

"I'm going to the store."

Retrieving the Extracted Phrases

You can access the extracted phrases from the doc._ attributes:

for prep_phrase in doc._.prep_phrases:
    print(prep_phrase)

Output:

near the river

Repeat for doc._.noun_phrases, doc._.verb_phrases, doc._.adj_phrases, and doc._.adv_phrases.

Retrieving the Normalized Text

The normalizer modifies the doc object itself, so you can access the normalized text as you usually would:

print(doc.text)

Output:

She said, "I am going to the store." The store is located near the river.

Retrieving the Extracted Triples

You can access the extracted triples from the doc._.triples attribute:

for triple in doc._.triples:
    print(triple)

Output:

('store', 'located near', 'river')

Retrieving the Entity Similarities and Mappings

You can access the entity similarities and mappings from the doc._.ent_similarity and doc._.ent_mappings attributes:

for ent1, ent2, similarity in doc._.ent_similarity:
    print(ent1, ent2, similarity)

for ent, mappings in doc._.ent_mappings.items():
    print(ent, mappings)