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.

翻译：植物学领域富含隐喻性术语，这些术语在花卉与植物的描述和识别中起着重要作用。然而，在语篇中识别这些术语是一项艰巨的任务，这往往导致翻译过程和词典编纂任务中出现错误。对于机器翻译而言，无论是单词术语还是多词术语，这一过程更具挑战性。近年来，自然语言处理应用和机器翻译技术关注的问题之一，是通过深度学习自动识别语篇中的隐喻词汇。本研究旨在通过利用十三个流行的基于Transformer的模型以及ChatGPT来填补这一空白，并证明判别式模型的表现优于GPT-3.5模型，其中最佳模型在花卉与植物隐喻名称识别任务中取得了92.2349%的F1分数。