Lexical Simplification: Making Text Easier to Read

Module: Natural Language Processing | Difficulty: Advanced

Lexical Simplification Pipeline

1. Identify complex words
2. Generate candidates
3. Rank by simplicity and meaning preservation

Complexity Prediction

Evaluation

| Metric | BLEU | Simplicity | Meaning | |--------|------|------------|---------| | Baseline | 65.2 | 52.3 | 78.1 | | BERT-based | 72.1 | 68.5 | 82.3 |

import numpy as np
from collections import Counter

def lexical_simplification(text, substitution_dict):
    words = text.split()
    simplified = []
    for word in words:
        if word.lower() in substitution_dict:
            simplified.append(substitution_dict[word.lower()])
        else:
            simplified.append(word)
    return ' '.join(simplified)

def predict_complexity(word, frequency_list):
    freq = frequency_list.get(word.lower(), 0)
    syllables = count_syllables(word)
    return 1 / (1 + np.exp(-(0.5 * np.log(freq + 1) + 0.3 * syllables)))

Research Insight: Lexical simplification requires balancing simplicity with meaning preservation. Frequency-based methods are simple but effective for common words. For rare words, context-aware methods using language models produce better substitutions.