# Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions

arXiv: https://arxiv.org/abs/1902.10667 · HTML: https://mlsanity.com/paper/1902.10667/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 5 of 5)

- [BERT(s) to Detect Multiword Expressions](https://mlsanity.com/paper/2208.07832/) (2022)
- [CoAM: Corpus of All-Type Multiword Expressions](https://mlsanity.com/paper/2412.18151/) (2024)
- [Binary Token-Level Classification with DeBERTa for All-Type MWE Identification: A Lightweight Approach with Linguistic Enhancement](https://mlsanity.com/paper/2601.19360/) (2026)
- [Transformer-based Detection of Multiword Expressions in Flower and Plant Names](https://mlsanity.com/paper/2209.08016/) (2022)
- [MWE as WSD: Solving Multiword Expression Identification with Word Sense Disambiguation](https://mlsanity.com/paper/2303.06623/) (2023)

## Abstract (arXiv)

We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontinuity, an under-explored aspect that poses a significant challenge to computational treatment of MWEs. Two neural architectures are explored: Graph Convolutional Network (GCN) and multi-head self-attention. GCN leverages dependency parse information, and self-attention attends to long-range relations. We finally propose a combined model that integrates complementary information from both through a gating mechanism. The experiments on a standard multilingual dataset for verbal MWEs show that our model outperforms the baselines not only in the case of discontinuous MWEs but also in overall F-score.