normeval 0.1.3

A scientific suite for evaluating text normalization algorithms.

normeval: Scientific Evaluation Suite for Text Normalization

normeval is a robust, mathematically grounded Python framework for evaluating text normalization algorithms. It goes beyond simple string matching by combining semantic preservation, vocabulary compression, character-level fidelity, and statistically rigorous downstream impact analysis.

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🔬 Motivation: Why do we need this?

Evaluating text normalization (such as stemming, lemmatization, spell-correction, or noise reduction) is traditionally a fragmented process. Researchers often rely solely on downstream accuracy, which is highly susceptible to dataset bias and random initialization.

A "good" normalization algorithm must balance two competing objectives:

1. Compression: It must collapse noisy variations of words into a single representation to reduce the feature space.
2. Preservation: It must not destroy the underlying semantic meaning or map unrelated words together.

normeval provides a holistic, unified suite to measure this balance, ensuring your normalization techniques are rigorously tested before being deployed in NLP pipelines.

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📦 Installation

Install directly from PyPI:

pip install normeval

🚀 Quick StartPythonfrom normeval import NormalizationEvaluator

from sklearn.linear_model import LogisticRegression
from sentence_transformers import SentenceTransformer

# 1. Your parallel datasets
texts_original = [
    "The cats are running!!",
    "A running cat..."
]

texts_normalized = [
    "the cat run",
    "a run cat"
]

labels = [1, 0]

# 2. Define your models
classifiers = [LogisticRegression()]
embedder = SentenceTransformer('all-MiniLM-L6-v2')

# 3. Initialize Evaluator
evaluator = NormalizationEvaluator(
    texts_original,
    texts_normalized,
    labels=labels,
    classifiers=classifiers,
    embedding_model=embedder
)

# 4. Run the full suite
results = evaluator.evaluate_all(lang="Global")

print(results)

📊 Metrics (Function Deep-Dive)

normeval evaluates text normalization across macroscopic, semantic, and downstream dimensions.

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1. Compression Ratio (CR)

• Function: calculate_cr()
• What it is: Measures the macroscopic reduction in vocabulary size.
• The Math:
  CR = |V_original| / |V_normalized|
• Interpretation:
  • CR = 1.0 → No compression occurred (bijective mapping).
  • CR > 1.0 → Multiple noisy variants successfully collapsed into fewer normalized forms.

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2. Information Retention Score (IRS)

• Function: calculate_irs(batch_size=32)
• What it is: Measures how much semantic meaning survived the normalization process.
• Mechanism: Converts both original and normalized texts into dense vector embeddings and computes paired cosine similarity.
• Interpretation:
  • Range: [-1, 1]
  • A score near 1.0 indicates semantic meaning was preserved successfully.

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3. Algorithm Effectiveness Score (AES)

• Function: calculate_aes(cr, irs)
• What it is: A harmonic mean balancing Compression (CR) and Preservation (IRS).
• The Math: VRG = 1-1/CR AES = (2 × IRS × VRG) / (IRS + VRG)
• Interpretation: Punishes algorithms that are:
  • Too aggressive (High CR, low IRS)
  • Too passive (High IRS, low CR)

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4. Average Normalized Levenshtein Distance (ANLD)

• Function: calculate_anld()
• What it is: A micro-level safety metric measuring character-level fidelity.
• The Math:
  ANLD = (1 / |V|) Σ [ LD(w, σ(w)) / |w| ]
• Where:
  • LD = Levenshtein Distance
  • σ(w) = normalized form of word w

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5. Model Performance Delta (MPD) & Statistical Significance

• Function: calculate_mpd(n_splits=5, average_method='weighted')
• What it is: Measures the impact on classification performance using Stratified N-Fold CV.
• Statistical Rigor: Applies the Wilcoxon Signed-Rank Test across CV folds (p < 0.05).
• Leakage Prevention: TfidfVectorizer is fit inside each CV fold to strictly prevent train/test leakage.

🏗️ Project Goals

Reproducible evaluation pipelines.

Statistically grounded benchmarking.

Semantic preservation analysis.

Language-agnostic evaluation.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, open an issue first to discuss proposed improvements.

📄 License

This project is licensed under the MIT License.

⭐ Citation If you use normeval in your research, please cite: underway