Papers with SPV
Enhanced Metaphor Detection via Incorporation of External Knowledge Based on Linguistic Theories (2021.findings-acl)
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| Challenge: | Existing methods for metaphor detection take little consideration on linguistic theories of metaphor detection. |
| Approach: | They propose two BERT-based models for metaphor detection based on examples and definitions of words from the Oxford Dictionary. |
| Outcome: | The proposed models achieve state-of-the-art performance on two established metaphor datasets and are highly interpretable. |
A Quantum-Inspired Matching Network with Linguistic Theories for Metaphor Detection (2024.lrec-main)
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| Challenge: | Metaphor identification procedures and selectional preference violations are challenging for machines to recognize and comprehend metaphors. |
| Approach: | They propose a quantum-inspired matching network for metaphor detection based on QLM . metaphors are widely present in the language, thought and behavior of humans . |
| Outcome: | The proposed method can be used to detect metaphors even in the face of conventional metaphors. |
Metaphor Detection via Linguistics Enhanced Siamese Network (2022.coling-1)
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| Challenge: | Empirical results indicate that MisNet achieves competitive performance on several datasets. |
| Approach: | They propose a model that converts linguistic rules into semantic matching tasks. |
| Outcome: | Empirical results show that MisNet achieves competitive performance on several datasets. |
EmbodiedBERT: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information (2024.findings-emnlp)
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| Challenge: | Existing methods for metaphor detection rely on heuristics such as Metaphor Identification Procedure (MIP) and Selection Preference Violation (SPV). |
| Approach: | They propose a cognitively motivated module that leverages the cognitive information of embodiment that can be derived from word embeddings and explicitly models the process of sensorimotor change that has been demonstrated as essential for metaphor processing. |
| Outcome: | The proposed module can improve metaphor detection compared with the heuristic MIP that has been applied previously. |