lingpatlab 1.1.1

Linguistic Pattern Lab using spaCy

LingPatLab

Linguistic Pattern Laboratory: Advanced NLP pipeline for text analysis, entity extraction, and pattern recognition.

Features

• Tokenization: Custom Graffl tokenizer with intelligent handling of contractions, abbreviations, and punctuation
• Parsing: Deep linguistic analysis with POS tagging, dependency parsing, and WordNet integration
• Entity Extraction: Pattern-based extraction of people and topics with anaphora resolution
• Segmentation: Paragraph and sentence boundary detection
• Rich Annotations: Sentiment, lemmatization, stemming, and morphological features

Installation

pip install lingpatlab

Quick Start

from lingpatlab import LingPatLab

api = LingPatLab()

# Parse text into structured tokens
sentence = api.parse_input_text("Admiral Nimitz commanded the Pacific Fleet.")
print(sentence.to_string())

# Extract people with anaphora resolution
text = "Admiral William Halsey led the fleet. Halsey was known for his aggressive tactics."
sentence = api.parse_input_text(text)
people = api.extract_people(sentence)
# Returns: {'Halsey': ['Admiral William Halsey', 'Halsey']}

# Extract topics and named entities
topics = api.extract_topics(sentence)

Usage Examples

Parse Multiple Lines

lines = [
    "The Battle of Midway was a turning point.",
    "Admiral Nimitz made crucial decisions."
]
sentences = api.parse_input_lines(lines)

for sentence in sentences:
    print(sentence.to_string())

Segmentation

from lingpatlab import segment_input_text

text = "First sentence. Second sentence. Third sentence."
segments = segment_input_text(text)
# Returns: ['First sentence.', 'Second sentence.', 'Third sentence.']

Access Token Details

sentence = api.parse_input_text("The quick brown fox jumps.")

for token in sentence:
    print(f"Text: {token.text}")
    print(f"POS: {token.pos}")
    print(f"Lemma: {token.normal}")
    print(f"Is WordNet: {token.is_wordnet}")
    print(f"Dependency: {token.dep}")

Data Classes

• Sentence: Single sentence with token list
• Sentences: Collection of sentences
• SpacyResult: Individual token with full linguistic annotation
• OtherInfo: Additional morphological and dependency metadata

Architecture

LingPatLab
├── tokenizer/     # Custom tokenization with Graffl
├── parser/        # spaCy integration + enhancements
├── analyzer/      # Entity extraction with pattern matching
├── segmenter/     # Sentence and paragraph segmentation
└── utils/         # WordNet, Porter stemmer, utilities

Requirements

• Python 3.10+
• spaCy 3.8.2
• spaCy model: en_core_web_sm

Development

# Install with dev dependencies
pip install -e ".[linting,testing]"

# Run tests
pytest

# Run regression suite
python regression/regression_runner.py

Links

• PyPI Package
• GitHub Repository
• Issue Tracker

License

MIT License - see LICENSE for details.

Author

Craig Trim - [e-mail]

More NLP articles and demos at craigtrim.com