advanced-text-processing 0.2.1

A powerful Named Entity Recognition and Resolution library with semantic matching

Advanced Text Processing

A powerful Named Entity Recognition (NER) and Entity Resolution library designed for complex text processing tasks. It combines state-of-the-art NLP models (spaCy, Transformers) with robust knowledge bases (Wikidata, WordNet) to provide accurate entity extraction, canonicalization, and semantic matching.

🚀 Features

• Advanced Entity Resolution:
  • Mode A (Sequential): Fast, early-stopping pipeline for high-confidence matches.
  • Mode B (Parallel): Aggregates multiple signals (fuzzy, semantic, contextual) for difficult cases.
• Semantic Matching: Maps inputs to canonical schemas using sentence embeddings (SentenceTransformers).
• Alias Retrieval: Automatically fetches aliases from Wikidata and synonyms from WordNet.
• Canonicalization:
  • Entities (e.g., "Apple" -> "Apple Inc.")
  • Relationships (e.g., "relies on" -> "depends_on")
  • Properties (e.g., "birth date" -> "date_of_birth")
• Flexible Candidate Generation: Supports exact lookup, fulltext blocking, and ANN search (FAISS/hnswlib).

📦 Installation

pip install advanced-text-processing

After installation, download the required models:

# Download spaCy model
python -m spacy download en_core_web_lg

# Download NLTK data
python -c "import nltk; nltk.download('wordnet'); nltk.download('omw-1.4')"

See Installation Guide for detailed instructions.

⚡ Quick Start

Named Entity Recognition

from ner_lib import recognize_entities

text = "Apple Inc. was founded by Steve Jobs in Cupertino."
result = recognize_entities(text)

for entity in result['entities']:
    print(f"{entity['text']} ({entity['type']})")
# Output:
# Apple Inc. (ORG)
# Steve Jobs (PERSON)
# Cupertino (GPE)

Entity Canonicalization

from ner_lib import canonicalize_entity

# Canonicalize an entity mention
result = canonicalize_entity("apple inc", mode="progressive")
print(f"Canonical: {result['canonical_name']}")
print(f"Aliases: {result['aliases']}")
# Output: 
# Canonical: Apple Inc.
# Aliases: ['Apple', 'AAPL', 'Apple Computer', ...]

Relationship Canonicalization

from ner_lib import Config, canonicalize_relationship

# Configure semantic matching
config = Config()
config.semantic_matching.enabled = True
config.semantic_matching.canonical_relationships = ["depends_on", "created_by"]

# Canonicalize a relationship phrase
result = canonicalize_relationship("relies heavily on", config=config)
print(f"Canonical: {result['canonical_name']}")
# Output: Canonical: depends_on

📚 Documentation

• Installation Guide
• API Reference
• System Architecture
• Contributing Guide
• Changelog

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details on how to get started.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgements

This library stands on the shoulders of giants. We gratefully acknowledge the following open-source projects:

• spaCy: For industrial-strength NLP.
• Sentence Transformers: For state-of-the-art text embeddings.
• Wikidata: For the comprehensive knowledge base.
• NLTK & WordNet: For lexical database support.

See ACKNOWLEDGEMENTS.md for the full list of dependencies and credits.