Deep Learning Methods for Extracting Metaphorical Names of Flowers and Plants
This addresses errors in translation and lexicography for botany, but is incremental as it applies existing deep learning methods to a specific domain.
The study tackled the problem of automatically identifying metaphorical names of flowers and plants in discourse, which is challenging for tasks like machine translation, and achieved a result of 92.2349% F1 score using transformer-based models, with discriminative models outperforming GPT-3.5.
The domain of Botany is rich with metaphorical terms. Those terms play an important role in the description and identification of flowers and plants. However, the identification of such terms in discourse is an arduous task. This leads in some cases to committing errors during translation processes and lexicographic tasks. The process is even more challenging when it comes to machine translation, both in the cases of single-word terms and multi-word terms. One of the recent concerns of Natural Language Processing (NLP) applications and Machine Translation (MT) technologies is the automatic identification of metaphor-based words in discourse through Deep Learning (DL). In this study, we seek to fill this gap through the use of thirteen popular transformer based models, as well as ChatGPT, and we show that discriminative models perform better than GPT-3.5 model with our best performer reporting 92.2349% F1 score in metaphoric flower and plant names identification task.