Abstract
Metaphors are a fascinating aspect of human language which facilitates optimal communication. It enables us to express an abstract idea or event with the help of a well-understood concept from another domain. Recently, there is a surge in interest of researchers from the cognitive domain as well as linguistics to process different types of metaphorical utterances in text effectively. In this paper, we reflect upon the problem of Type-III metaphor detection which occurs in the form of \({<}\text {adjective},\,\text {noun}{>}\) in natural text. Prior works have predominantly used word embeddings with different algorithms and datasets to detect these metaphorical instances. However, there is a need to analyze if there is any significant advantage of using techniques such as Neural Network over traditional models such as SVM. In this paper, we perform a qualitative analysis to understand the efficacy of different algorithms in detecting Type-III metaphors. We perform experiments on two publicly available datasets. Our results indicate that given a large dataset of training, the models trained using different algorithms provide a comparable performance.
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Notes
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Google Translate: https://translate.google.co.in/
- 2.
WordNet search(sweet): https://goo.gl/8cxvns
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Rai, S., Chakraverty, S., Garg, A. (2018). Effect of Classifiers on Type-III Metaphor Detection. In: Chakraverty, S., Goel, A., Misra, S. (eds) Towards Extensible and Adaptable Methods in Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-2348-5_18
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DOI: https://doi.org/10.1007/978-981-13-2348-5_18
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