GLeMM: A large-scale multilingual dataset for morphological research

TL;DR

GLeMM is a large, multilingual, automatically annotated morphological dataset from Wiktionary, designed to facilitate data-driven research and computational modeling of derivational morphology across seven European languages.

Contribution

It introduces GLeMM, a novel, extensive, automated morphological resource with semantic annotations, enabling new research in form-meaning relations in derivational morphology.

Findings

GLeMM covers seven European languages.

The dataset includes automatic morphological feature annotations.

Semantic descriptions are provided for a subset of entries.

Abstract

In derivational morphology, what mechanisms govern the variation in form-meaning relations between words? The answers to this type of questions are typically based on intuition and on observations drawn from limited data, even when a wide range of languages is considered. Many of these studies are difficult to replicate and generalize. To address this issue, we present GLeMM, a new derivational resource designed for experimentation and data-driven description in morphology. GLeMM is characterized by (i) its large size, (ii) its extensive coverage (currently amounting to seven European languages, i.e., German, English, Spanish, French, Italian, Polish, Russian, (iii) its fully automated design, identical across all languages, (iv) the automatic annotation of morphological features on each entry, as well as (v) the encoding of semantic descriptions for a significant subset of these entries. It enables researchers to address difficult questions, such as the role of form and meaning in word-formation, and to develop and experimentally test computational methods that identify the structures of derivational morphology. The article describes how GLeMM is created using Wiktionary articles and presents various case studies illustrating possible applications of the resource.