ConceptFrameMet: Metaphor Detection with Frame and Source Domain Prediction

A comprehensive metaphor detection model that predicts semantic frames and source domains

Model Description

ConceptFrameMet is a metaphor detection model which not only detects metaphors but also predicts:

1. Metaphor Classification: Whether a target word is used metaphorically or literally
2. Semantic Frames: The conceptual frame evoked by the target word
3. Source Domains: The source domain of the metaphor (for metaphorical uses)

Please see https://github.com/julia-nixie/ConceptFrameMet and PAPER LINK for details.

Model Architecture

• Base Model: RoBERTa-base
• Architecture: MelBERT with adaptive source domain integration
• Training Data: VUA18 metaphor corpus
• Configuration:
  • Source blend mode: replacement
  • Source use mode: metaphor_only
  • Metaphor threshold: 0.5

Performance

Evaluated on standard metaphor detection benchmarks:

Dataset	F1 Score	Accuracy
VUA18	0.767	0.930
MOH-X	0.814	0.803
TroFi	0.633	0.605

Quick Start

Installation

pip install transformers torch

Basic Usage

from transformers import RobertaTokenizer
import torch

# Load model and tokenizer
model_path = "nixie1981/ConceptFrameMet"
tokenizer = RobertaTokenizer.from_pretrained(model_path)

# Example sentence
sentence = "The company is navigating through troubled waters"
target_word = "navigating"

# Predict metaphor with frame and source
result = predict_metaphor(sentence, target_word)

print(f"Is Metaphor: {result['is_metaphor']}")
print(f"Confidence: {result['metaphor_confidence']:.2f}")
print(f"Semantic Frame: {result['frame']}")
print(f"Source Domain: {result['source']}")

Expected Output

Is Metaphor: True
Confidence: 0.92
Semantic Frame: Self_motion
Source Domain: JOURNEY

Use Cases

1. Metaphor Detection: Identify metaphorical language in text
2. Frame Analysis: Understand conceptual frames in discourse
3. Source Mapping: Identify source-target domain mappings
4. Literary Analysis: Analyze figurative language patterns
5. Education: Teaching metaphor comprehension

Model Inputs

The model expects:

• sentence: The full sentence containing the target word
• target_word: The specific word to analyze for metaphor

Model Outputs

The model returns a dictionary with:

• is_metaphor: Boolean indicating if the target is metaphorical
• metaphor_confidence: Confidence score for metaphor prediction (0-1)
• frame: Predicted semantic frame
• frame_confidence: Confidence for frame prediction
• source: Predicted source domain (for metaphors)
• source_confidence: Confidence for source prediction

Citation

If you use this model in your research, please cite:

@misc{conceptframemet2026,
  title={ConceptFrameMet: Metaphor Detection with Frame and Source Domain Prediction},
  author={Your Name},
  year={2026},
  url={https://huggingface.co/YOUR_USERNAME/ConceptFrameMet}
}

Contact

For questions or issues, please open an issue on the model repository or contact [your email].