Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classification
📰 ArXiv cs.AI
Learn to classify multiword expressions using supervision and demonstration-based in-context learning methods for Turkish idiomatic light verb constructions
Action Steps
- Frame Turkish LVC detection as a binary classification task
- Create a manually controlled dataset with matched negatives
- Evaluate supervision versus demonstration-based in-context learning methods
- Compare the performance of both methods on the classification task
- Apply the best-performing method to real-world Turkish LVC classification tasks
Who Needs to Know This
NLP researchers and engineers working on multiword expression classification tasks can benefit from this study to improve their models' performance on Turkish idiomatic light verb constructions
Key Insight
💡 Demonstration-based in-context learning can be an effective approach for multiword expression classification, especially when labeled data is limited
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🤖 Classify Turkish idiomatic light verb constructions with supervision and demonstration-based in-context learning! 📊
Key Takeaways
Learn to classify multiword expressions using supervision and demonstration-based in-context learning methods for Turkish idiomatic light verb constructions
Full Article
Title: Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classification
Abstract:
arXiv:2606.07479v1 Announce Type: cross Abstract: Turkish idiomatic light verb constructions (LVCs) are challenging for multiword expression processing because they often share the same surface form as fully literal verb-object combinations while functioning as a single, partially idiomatic predicate. We frame Turkish LVC detection as a binary classification task (literal meaning vs. idiomatic meaning) and evaluate on a manually created controlled set (N=147) with matched negatives: out-of-domai
Abstract:
arXiv:2606.07479v1 Announce Type: cross Abstract: Turkish idiomatic light verb constructions (LVCs) are challenging for multiword expression processing because they often share the same surface form as fully literal verb-object combinations while functioning as a single, partially idiomatic predicate. We frame Turkish LVC detection as a binary classification task (literal meaning vs. idiomatic meaning) and evaluate on a manually created controlled set (N=147) with matched negatives: out-of-domai
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