Arabic metaphor annotation guideline improves figurative language detection
AraMIP: Extending MIPVU Towards Metaphor Identification in Arabic
Computation and Language
Summary
Figurative language like metaphors is tricky to detect in Arabic because of the language's unique features. The authors created AraMIP, a new step-by-step guide to identify metaphors, similes, and indirect expressions in Arabic text. They tested AraMIP on a set of 300 sentences, revealing challenges like Arabic’s complex word forms and unclear dictionary meanings. This guide is a starting point to build bigger datasets for better language tools in Arabic.
What this means in practice
- •For arabic nlp developers: Create better tools for recognizing metaphors and figurative speech in Arabic text using the AraMIP annotation framework.
- •For language technology companies: Improve Arabic language understanding in AI by training models on the newly annotated figurative language dataset derived from AraMIP.$Commercial implications: Allows companies to develop advanced Arabic AI applications that handle figurative language nuances better than before.
Tested on one dataset.
Authors
Mandar Marathe, Manar Ali, Sara Nabhani, Raia Abu Ahmad, Ibrahim Baroud, Omar Momen
Abstract
Metaphor research has gained increasing attention due to its relevance to linguistic creativity, language use, cognitive processes, and related areas. While many efforts have been devoted to metaphor identification and annotation in English and other languages, Arabic remains under-resourced in this area. In this work, we propose the Arabic Metaphor Identification Procedure (AraMIP), a novel guideline for Arabic metaphor annotation. AraMIP builds on the widely used Metaphor Identification Procedure Vrije Universiteit (MIPVU) framework, incorporating adaptations that accounts for the language-specific properties of Arabic. We distinguish three major types of Arabic figurative language: Isti'ara (metaphor), kinaya (metonymy/indirect expression), and tashbih (simile), and annotate a pilot dataset of 300 sentences (5277 words). Our analysis reveals key challenges specific to Arabic, including morphological complexity, inconsistencies in dictionary sense ordering, and the absence of standardized contextual materials for annotators. This work contributes a first step toward standardized Arabic figurative instances and facilitates the development of larger annotated resources, thereby supporting future research on figurative language in Arabic.