star092304/CEFR-Annotated-WordNet

CEFR-Annotated WordNet: LLM-Based Proficiency-Guided Semantic Database for Language Learning

Paper: CEFR-Annotated WordNet: LLM-Based Proficiency-Guided Semantic Database for Language Learning

Authors: Masato Kikuchi, Masatsugu Ono, Toshioki Soga, Tetsu Tanabe, Tadachika Ozono

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Overview

CEFR-Annotated WordNet is a comprehensive semantic database that maps WordNet 3.0 senses and SemCor 3.0 instances to CEFR (Common European Framework of Reference for Languages) proficiency levels. This dataset enables LLM-based analysis of word complexity and is specifically designed for language learning applications.

The dataset provides multiple data formats:

• Lookup tables for sense-level and instance-level CEFR annotations
• Fine-tuning datasets (CSV and JSONL) for training models on CEFR proficiency assessment
• Two specialized model fine-tuning splits: DeBERTa and LLM formats

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Directory Structure

CEFR-Annotated-WordNet/
├── cefr-level_annotation/        # Core annotation files
│   ├── semcor_instance_cefr.tsv  # SemCor instance → CEFR mapping
│   └── wordnet_sensekey_cefr.tsv # WordNet sense key → CEFR mapping
├── fine_tuning_debert/            # DeBERTa model fine-tuning data
│   └── semcor_mapped.csv         # CSV format with context and labels
├── fine_tuning_llm/               # LLM model fine-tuning data
│   ├── train.jsonl               # Training split (chat format)
│   └── valid.jsonl               # Validation split (chat format)
├── LICENSES/                      # License files
│   ├── CC-BY-4.0.txt
│   ├── WORDNET-3.0-LICENSE.txt
│   └── SEMCOR-3.0-LICENSE.txt
├── NOTICE                         # Attribution and copyright notice
└── README.md                      # This file

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Dataset Files

1. CEFR-Level Annotation Files (cefr-level_annotation/)

These are core lookup tables mapping linguistic units to CEFR proficiency levels.

semcor_instance_cefr.tsv

• Format: Tab-separated values (TSV)
• Columns:
  • instance_id: SemCor instance identifier (format: d###.s###.t###)
  • CEFR_level: CEFR proficiency level (A1, A2, B1, B2, C1, C2)
• Example entries:

  d000.s000.t000	A1
  d000.s000.t002	C1
  d000.s000.t003	B2
  d000.s001.t004	B2
  

• Use case: Map SemCor instances to proficiency levels for corpus-based analysis or instance-level sense disambiguation.
• Size: Comprehensive coverage of SemCor 3.0 instances

wordnet_sensekey_cefr.tsv

• Format: Tab-separated values (TSV)
• Columns:
  • sensekey: WordNet sense key (format: lemma%pos:sense:lexfile:sense_number)
  • CEFR_level: CEFR proficiency level (A1, A2, B1, B2, C1, C2)
• Example entries:

  a.m.%4:02:00::	A1
  abandon%2:31:00::	B2
  abandon%2:31:01::	C1
  ability%1:07:00::	B1
  able%5:00:00:competent:00	C2
  

• Use case: Direct lookup for sense-level proficiency assessment in WordNet-based applications, lexical complexity estimation, and vocabulary ranking.
• Size: Complete WordNet 3.0 sense coverage

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2. DeBERTa Fine-Tuning Data (fine_tuning_debert/)

This directory contains data formatted for training masked language models like DeBERTa.

semcor_mapped.csv

• Format: Comma-separated values (CSV) with headers
• Columns:
  • instance_id: WordNet sense key identifier
  • sentence: Full sentence context from SemCor
  • target_word: The word to be classified
  • lemma: Lemmatized form of the target word
  • cefr_level: Target CEFR proficiency level (A1-C2)
• Example entry:

  say%2:32:00::,The Fulton County Grand Jury said Friday an investigation of Atlanta 's recent primary election produced `` no evidence '' that any irregularities took place .,said,said,B1
  investigation%1:09:00::,The Fulton County Grand Jury said Friday an investigation of Atlanta 's recent primary election produced `` no evidence '' that any irregularities took place .,investigation,investigation,B2
  

• Use case: Direct training data for sequence classification models like DeBERTa v3 for CEFR level prediction.
• Data characteristics:
  • Sentences from real SemCor corpus
  • Single target word per row with full context
  • Balanced CEFR level distribution across training samples

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3. LLM Fine-Tuning Data (fine_tuning_llm/)

This directory contains data formatted for instruction-following models and large language models.

train.jsonl

• Format: JSON Lines (one JSON object per line)
• Structure: Chat-style messages format compatible with GPT, Claude, and other LLMs
• Fields per entry:
  • messages: Array containing conversational turns with roles and content
    • role: One of system, user, assistant
    • content: Text content for each role
• Example structure:

  {
    "messages": [
      {
        "role": "system",
        "content": "You are an expert in assessing English vocabulary proficiency levels according to the CEFR framework..."
      },
      {
        "role": "user",
        "content": "Assess the CEFR level of the word 'investigate' in this sentence: \"The Fulton County Grand Jury said Friday an investigation of Atlanta's recent primary election produced no evidence...\"\n\nProvide only the CEFR level (A1, A2, B1, B2, C1, C2)."
      },
      { "role": "assistant", "content": "B2" }
    ]
  }
  

• Use case: Training LLMs for CEFR proficiency assessment through instruction fine-tuning (LoRA, QLoRA, etc.).
• Size: Training split with thousands of examples
• Data characteristics:
  • System prompt defining the CEFR assessment task
  • Contextual sentences from SemCor corpus
  • Target words with clear proficiency labels

valid.jsonl

• Format: Same JSON Lines format as train.jsonl
• Purpose: Validation/evaluation split for monitoring model performance during training
• Use case: Evaluate CEFR classification accuracy during fine-tuning iterations
• Size: Validation subset (typically 10-20% of training data)

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CEFR Proficiency Levels

The dataset uses the Common European Framework of Reference for Languages (CEFR) scale with 6 levels:

Level	Description	Example
A1	Beginner	Basic words: "is", "the", "be", "end"
A2	Elementary	Common everyday vocabulary: "have", "say", "day"
B1	Intermediate	General vocabulary with some complexity: "ability", "investigate", "evidence"
B2	Upper Intermediate	Advanced general vocabulary: "abandon", "jury", "produce"
C1	Advanced	Sophisticated vocabulary: "abnormal", "aboard", "abandon" (specialized senses)
C2	Proficiency	Rare, highly specialized vocabulary: "able" (competent sense), "oblige", "aberrant"

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Use Cases

1. Lexical Complexity Assessment

Use the sense-level mappings (wordnet_sensekey_cefr.tsv) to analyze vocabulary complexity in texts.

2. CEFR-Based Word Sense Disambiguation

Combine with WordNet to disambiguate word senses while assessing proficiency levels.

3. Language Learning Applications

• Generate vocabulary recommendations based on CEFR levels
• Create proficiency-matched reading materials
• Build adaptive learning systems

4. Model Training

• Fine-tune DeBERTa using semcor_mapped.csv for sequence classification
• Instruction-tune LLMs using train.jsonl and valid.jsonl for CEFR assessment

5. Academic Research

• Study vocabulary distribution across proficiency levels
• Analyze sense-level complexity patterns
• Build datasets for NLP research on complexity assessment

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Data Statistics

File	Entries	Coverage
wordnet_sensekey_cefr.tsv	Complete WordNet 3.0 senses	All synsets in WordNet
semcor_instance_cefr.tsv	All SemCor instances	SemCor 3.0 corpus
semcor_mapped.csv	Thousands of sense instances	Representative SemCor sentences
train.jsonl	Large training split	Diverse vocabulary and contexts
valid.jsonl	Validation samples	Held-out evaluation set

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Licenses

This dataset combines multiple licensed resources:

• Our CEFR Annotations & Splits: CC BY 4.0
  • Applies to: CEFR labels, dataset splits, and novel annotations
  • Files: All CEFR labels in .tsv, .csv, and .jsonl files
• WordNet 3.0 (Princeton University): WordNet License
  • Applies to: Sense keys and WordNet identifiers
• SemCor 3.0 (Princeton University): SemCor License
  • Applies to: SemCor instance IDs and sentence text

Important: When using this dataset:

1. Cite the original paper (see header above)
2. Comply with WordNet and SemCor licenses
3. Respect CC BY 4.0 requirements for attribution

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Citation

If you use this dataset in your research, please cite the original paper:

@article{kikuchi2024cefr,
  title={CEFR-Annotated WordNet: LLM-Based Proficiency-Guided Semantic Database for Language Learning},
  author={Kikuchi, Masato and Ono, Masatsugu and Soga, Toshioki and Tanabe, Tetsu and Ozono, Tadachika},
  journal={arXiv preprint arXiv:2510.18466},
  year={2024}
}

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Getting Started

Quick Start with Python

import pandas as pd

# Load sense-level CEFR mappings
sensekey_df = pd.read_csv('cefr-level_annotation/wordnet_sensekey_cefr.tsv', sep='\t')
print(f"Total senses: {len(sensekey_df)}")

# Load instance-level mappings
instance_df = pd.read_csv('cefr-level_annotation/semcor_instance_cefr.tsv', sep='\t')
print(f"Total instances: {len(instance_df)}")

# Load fine-tuning data for DeBERTa
train_df = pd.read_csv('fine_tuning_debert/semcor_mapped.csv')
print(f"Training examples: {len(train_df)}")

# Load LLM fine-tuning data
import json
llm_train = [json.loads(line) for line in open('fine_tuning_llm/train.jsonl')]
print(f"LLM training examples: {len(llm_train)}")

Training a DeBERTa Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from datasets import Dataset

# Load data
df = pd.read_csv('fine_tuning_debert/semcor_mapped.csv')
dataset = Dataset.from_pandas(df)

# Prepare for training
tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-base')
model = AutoModelForSequenceClassification.from_pretrained('microsoft/deberta-v3-base', num_labels=6)

# Continue with your training pipeline...

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FAQ

Q: Can I use this dataset commercially? A: Yes, as long as you comply with the CC BY 4.0 license (attribution required).

Q: How is the CEFR level assigned to each sense? A: Please refer to the original paper for the annotation methodology.

Q: Can I contribute improvements or corrections? A: We welcome feedback! Please refer to the NOTICE file for contact information.

Q: What's the difference between instance-level and sense-level annotations? A: Sense-level (wordnet_sensekey_cefr.tsv) provides a fixed mapping for each WordNet sense. Instance-level (semcor_instance_cefr.tsv) provides CEFR labels for specific word uses in context from the SemCor corpus.

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Contact & References

For questions or feedback about this dataset, please contact the authors through the information provided in the original paper: CEFR-Annotated WordNet: LLM-Based Proficiency-Guided Semantic Database for Language Learning

Related Resources:

• WordNet 3.0
• SemCor 3.0
• CEFR Framework