ContrastWSD: Enhancing Metaphor Detection with Word Sense Disambiguation

Core Concepts

Integrating Word Sense Disambiguation with the Metaphor Identification Procedure enhances metaphor detection models.

Abstract

Introduction to metaphors and their importance in communication. Evolution of metaphor detection methods from manual efforts to transformer-based models. The significance of integrating Word Sense Disambiguation (WSD) in metaphor detection. Comparison of ContrastWSD model with other baseline models on various benchmark datasets. Detailed methodology of ContrastWSD model including data augmentation and model structure. Experimental results showcasing the superior performance of ContrastWSD in detecting metaphors. Case studies demonstrating the effectiveness of ContrastWSD in identifying both conventional and novel metaphors.

Stats

"Our proposed method is evaluated on established benchmark datasets, and the results demonstrate significant improvements." "Our model consistently achieves superior (and occasionally comparable) precision, recall, and F1 scores when compared to other recent and robust metaphor detection models."

Quotes

"By utilizing the word senses derived from a WSD model, our model enhances the metaphor detection process." "Our proposed method outperforms other methods that rely solely on contextual embeddings or integrate only the basic definitions."

Key Insights Distilled From

ContrastWSD

by Mohamad Elzo... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2309.03103.pdf

Deeper Inquiries

How can integrating commonsense models like COMET enhance metaphor detection further?

Integrating commonsense models like COMET can enhance metaphor detection by providing a deeper understanding of the underlying concepts and relationships between words. COMET, which focuses on automatic knowledge graph construction, can help in capturing the implicit meanings and associations that may not be explicitly stated in the text. By incorporating common sense knowledge into metaphor detection, the model can better differentiate between literal and figurative language based on contextual cues and background information. This integration enables a more nuanced analysis of metaphors, especially those that rely on subtle or indirect connections between words.

What are potential limitations or biases introduced by relying heavily on external knowledge sources for metaphor detection?

Relying heavily on external knowledge sources for metaphor detection may introduce several limitations and biases. One limitation is the accuracy and coverage of the external knowledge base used. If the external source contains incomplete or inaccurate information, it could lead to errors in identifying metaphors or misinterpreting their meanings. Additionally, there is a risk of introducing bias from the external source itself, as certain definitions or interpretations may reflect specific cultural or linguistic perspectives. Another potential limitation is scalability and generalization across different domains or languages. External knowledge sources are often domain-specific or limited to certain languages, which could restrict the model's ability to detect metaphors accurately across diverse contexts. Moreover, relying too heavily on external sources may overshadow contextual clues present within the text itself, leading to an over-reliance on predefined definitions rather than dynamic interpretation based on context.

How might ContrastWSD's approach impact broader NLP tasks beyond just metaphor detection?

ContrastWSD's approach has implications beyond just metaphor detection as it emphasizes contrasting word senses with basic definitions to enhance understanding within a given context. This methodology could benefit various NLP tasks that require semantic analysis and disambiguation of words based on their usage within specific contexts. Word Sense Disambiguation (WSD): The integration of WSD techniques in ContrastWSD can improve word sense disambiguation tasks by considering both contextual meaning and basic definitions simultaneously. This approach could lead to more accurate identification of word senses in ambiguous sentences across different applications such as machine translation and information retrieval. Sentiment Analysis: In sentiment analysis tasks where understanding subtle nuances in language is crucial for determining sentiment polarity accurately, ContrastWSD's focus on contrasting word senses could aid in capturing figurative expressions related to emotions effectively. Text Paraphrasing: When generating paraphrases for textual content, having a clear distinction between literal meanings and figurative uses of words can help produce more diverse paraphrases that maintain original intent while offering alternative expressions through appropriate use of metaphors. 4 .Machine Translation: Enhancing machine translation systems with ContrastWSD's methodology could improve translations by ensuring that idiomatic phrases or culturally specific expressions are translated appropriately based on their intended meaning rather than solely relying on direct translations without considering connotations. These applications demonstrate how ContrastWSD's approach towards analyzing word senses within context can have far-reaching benefits across various NLP tasks beyond just metaphor detection.