Transformer-based Detection of Multiword Expressions in Flower and Plant Names

TL;DR

This paper investigates the use of transformer models for detecting multiword expressions in flower and plant names, demonstrating their superiority over LSTM-based models in this domain.

Contribution

It introduces the application of transformer-based neural models to MWE detection in botanical names, showing improved performance over prior LSTM models.

Findings

Transformer models outperform LSTM models in MWE detection accuracy.

Evaluation conducted on a new dataset from Encyclopedia of Plants and Flower.

Transformers show promise for domain-specific NLP tasks.

Abstract

Multiword expression (MWE) is a sequence of words which collectively present a meaning which is not derived from its individual words. The task of processing MWEs is crucial in many natural language processing (NLP) applications, including machine translation and terminology extraction. Therefore, detecting MWEs in different domains is an important research topic. In this paper, we explore state-of-the-art neural transformers in the task of detecting MWEs in flower and plant names. We evaluate different transformer models on a dataset created from Encyclopedia of Plants and Flower. We empirically show that transformer models outperform the previous neural models based on long short-term memory (LSTM).