White Paper: Large-Scale English Word Collection and Cleaning Pipeline

Abstract

This document outlines the design, methodology, and implementation of a large-scale English word collection, enhancement, and cleaning system. The system leverages prefix-based word collection, AI-driven semantic enhancement, and strict validation/cleaning rules to construct a massive English–Korean dictionary with metadata useful for downstream AI and NLP applications.

---

1. Introduction

Building a comprehensive wordlist is essential for natural language processing (NLP), educational tools, and AI-driven language models. Traditional dictionaries are limited by scope and update cycles. Our system introduces a dynamic, automated pipeline capable of:

• Collecting English words using adaptive prefix strategies.
• Enhancing words with Korean translations, part-of-speech tags, age-level classifications, and example sentences.
• Cleaning and validating entries with configurable rules.

---

2. System Architecture

2.1 Components

• Word Collector (011.py): Gathers candidate English words using prefix expansion and an Ollama-backed model.
• Word Enhancer: Enriches words with Korean meaning, age-group classification, part-of-speech, and examples.
• Data Cleaner (data_cleaner.py): Applies validation, normalization, and deduplication to produce a high-quality dataset.

2.2 Workflow Sequence

1. Collection Phase
   • Generate prefixes (adaptive, 2-letter, or 3-letter).
   • Query the model for words matching each prefix.
   • Store results with timestamps and metadata.
2. Enhancement Phase
   • Process words in chunks.
   • Request Korean translations, part-of-speech, and usage examples.
   • Merge enhanced data with base word entries.
3. Cleaning Phase
   • Validate words with configurable rules.
   • Remove duplicates, invalid forms, and undesired patterns.
   • Generate distribution statistics (letter frequencies, age groups, etc.).
4. Output
   • Store datasets in JSON format with metadata.
   • Provide CLI tools for analyzing patterns and cleaning data.

---

3. Validation Rules

Words are accepted or rejected based on the following constraints:

• Length: Must be within 2–50 characters (configurable).
• Character set: Alphabet-only; optional allowance for hyphen and apostrophe.
• Repetition: Rejects words with repeated character runs ≥ N (default 3).
• Blacklist: Regex-based exclusion of offensive or unwanted terms.
• Edge rules: No leading/trailing hyphens or apostrophes.
• Special rules: No double specials (e.g., --, '').

---

4. Metadata and Statistics

Each processed dataset includes metadata:

• Counts: Original, cleaned, removed, duplicates.
• Removal statistics: Breakdown by reason (e.g., too short, repeated chars).
• Letter distribution: Frequency of first letters A–Z.
• Age group distribution: For enhanced dictionaries.
• Timestamps: Collection, enhancement, and cleaning dates.

---

5. Command Line Interface (CLI)

Data Cleaner

# Analyze problematic patterns
python3 data_cleaner.py analyze words.json

# Clean dataset
python3 data_cleaner.py clean words.json -o cleaned.json

Dictionary Generator

# Stage 1: Collect words
python3 011.py --stage 1 --mode adaptive --batch 30

# Stage 2: Enhance words
python3 011.py --stage 2 --file massive_wordlist_xxx.txt --chunk 50

---

6. Applications

• NLP Research: Provides a scalable dataset for language model training.
• Education: Supplies graded vocabulary lists by age group.
• AI Assistants: Enables contextual translation and simplified explanations.
• Lexicography: Supports automated dictionary building and updates.

---

7. Conclusion

This system creates a bridge between raw, large-scale word collection and refined, structured dictionaries. By combining automated collection, semantic enrichment, and rigorous cleaning, it produces a dataset suitable for high-quality AI-driven applications.

Future work includes:

• Expansion with multi-language support.
• Integration with vector embeddings for semantic similarity.
• Continuous updates with crowdsourced validation.

---