# SHOMA at Parseme Shared Task on Automatic Identification of VMWEs: Neural Multiword Expression Tagging with High Generalisation

arXiv: https://arxiv.org/abs/1809.03056 · HTML: https://mlsanity.com/paper/1809.03056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Improving Automatic Hate Speech Detection with Multiword Expression Features](https://mlsanity.com/paper/2106.00237/) (2021)

## Abstract (arXiv)

This paper presents a language-independent deep learning architecture adapted to the task of multiword expression (MWE) identification. We employ a neural architecture comprising of convolutional and recurrent layers with the addition of an optional CRF layer at the top. This system participated in the open track of the Parseme shared task on automatic identification of verbal MWEs due to the use of pre-trained wikipedia word embeddings. It outperformed all participating systems in both open and closed tracks with the overall macro-average MWE-based F1 score of 58.09 averaged among all languages. A particular strength of the system is its superior performance on unseen data entries.