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

Abstract

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.

References in corpus (2)

• Natural Language Processing (almost) from Scratch
• Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks

Cited by in corpus (5)

• Similarity of Precursors in Solid-state Synthesis as Text-Mined from Scientific Literature
• MTLB-STRUCT @PARSEME 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models
• Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions
• To Be or Not To Be a Verbal Multiword Expression: A Quest for Discriminating Features
• Improving Automatic Hate Speech Detection with Multiword Expression Features