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。这些术语在花卉植物的描述与识别中发挥着重要作用。然而，在话语中识别此类术语是一项艰巨的任务，这有时会导致翻译过程及词典编纂任务中出现错误。当涉及机器翻译时，无论是单个词项还是多词项，这一过程更具挑战性。自然语言处理（NLP）应用与机器翻译（MT）技术近年来关注的重点之一，便是通过深度学习（DL）自动识别话语中基于隐喻的词汇。本研究旨在通过利用十三种流行的基于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——我们的最佳模型在花卉植物隐喻名称识别任务中取得了92.2349%的F1分数。