wenmin-wu/nlp-readability-metric-features

Readability Metric Features

Overview

Established readability formulas compute text complexity from sentence length, syllable counts, and word difficulty. Extract these as features for regression or classification tasks involving text quality, difficulty, or grade level. The textstat library provides a unified API for all major formulas.

Quick Start

import textstat

def extract_readability(text):
    return {
        "flesch_reading_ease": textstat.flesch_reading_ease(text),
        "flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
        "gunning_fog": textstat.gunning_fog(text),
        "ari": textstat.automated_readability_index(text),
        "coleman_liau": textstat.coleman_liau_index(text),
        "dale_chall": textstat.dale_chall_readability_score(text),
        "linsear_write": textstat.linsear_write_formula(text),
        "text_standard": textstat.text_standard(text, float_output=True),
    }

df["readability"] = df["text"].apply(extract_readability)
features = pd.json_normalize(df["readability"])

Workflow

1. Install textstat (pip install textstat)
2. Apply each readability formula to every text sample
3. Combine scores into a feature dataframe
4. Use as standalone features for GBDT or as auxiliary features alongside embeddings

Key Decisions

• Which formulas: Flesch + Gunning FOG + ARI cover most variance; others add marginal value
• Correlation: Many formulas are correlated — use with tree models (handle collinearity)
• Complement embeddings: These capture surface complexity; embeddings capture semantics
• Short texts: Readability formulas are unreliable below ~50 words

References

• CommonLit Readability Prize (Kaggle)
• Source: commonlit-readability-prize-eda-baseline