Transformer-based Detection of Multiword Expressions in Flower and Plant Names
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).
Keywords
Cite
@article{arxiv.2209.08016,
title = {Transformer-based Detection of Multiword Expressions in Flower and Plant Names},
author = {Damith Premasiri and Amal Haddad Haddad and Tharindu Ranasinghe and Ruslan Mitkov},
journal= {arXiv preprint arXiv:2209.08016},
year = {2022}
}
Comments
Submitted to The 5th Workshop on Multi-word Units in Machine Translation and Translation Technology at Europhras2022