GLeMM: A large-scale multilingual dataset for morphological research

Hathout Nabil; Basilio Calderone; Fiammetta Namer; Franck Sajous

cs.CLApr 14, 2026

GLeMM: A large-scale multilingual dataset for morphological research

Authors: Hathout Nabil, Basilio Calderone, Fiammetta Namer, Franck Sajous

Organizations: CLLE, Comue de Toulouse · CLLE, CNRS, Université Toulouse Jean Jaurès, Toulouse, France · CLLE, UBM · CLLE Montaigne, CNRS, Université Bordeaux Montaigne, Pessac, France ·

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.

Figures & tables

	GLAWI		Kaikki		MorphyNet
	definitions	morphological	definitions	morphological	entries
		section		section
English	yes	yes	yes	yes	yes
French	yes	yes	yes	yes	yes
Italian	yes	yes			yes
German			yes	yes	yes

Table 1: Source of the candidate pairs used to create the GLeMM lexicons

A	B	C	D	left pattern	right pattern
positivist	positivism	feminist	feminism	ˆ (.+)ist$	ˆ (.+)ism$
positivist	positivism	feminist	feminism	ˆ (.+)i(.+)ist$	ˆ (.+)i(.+)ism$
positivist	positivism	activist	activism	ˆ (.+)itivist$	ˆ (.+)tivism$
positivist	positivism	activist	activism	ˆ (.+)tivist$	ˆ (.+)tivism$
positivist	positivism	activist	activism	ˆ (.+)ivist$	ˆ (.+)ivism$
positivist	positivism	activist	activism	ˆ (.+)vist$	ˆ (.+)vism$

Table 2: Examples of alternation patterns yielded by analogies involving the pair positivist N and positivism N

lemma1	cat1	lemma2	cat2	stem	exponent1	exponent2
rastrero	A	rastreramente	R	rastrer	ˆ (.+)o$	ˆ (.+)amente$
‘despicable.M’		‘despicably’
ducha	N	duchita	N	duch	ˆ (.+)a$	ˆ (.+)ita$
‘shower’		‘small shower’
talentoso	A	talento	N	talento	ˆ (.+)so$	ˆ (.+)$
‘talented.M’		‘talent’

Table 3: Examples of entries in the Spanish pairs.tsv table

lemma1	cat1	lemma2	cat2	stem	exponent1	exponent2
sensitivity	N	electrosensitivity	N	sensitivity	ˆ (.+)$	ˆ electro(.+)$
clonally	R	subclonally	R	clonally	ˆ (.+)$	ˆ sub(.+)$
supertechnological	A	technology	N	technolog	ˆ super(.+)ical$	ˆ (.+)y$

Table 4: Examples of entries in the English pairs.tsv table

a.		spry A spryness N spry ˆ (.+) $ˆ (.+)ness$
	definition2	The property of being spry.
	lemmatized_definition2	the property of be spry .
b.		astounding A unastounding A astounding ˆ (.+) $ˆ un(.+)$
	definition2	Not astounding.
	lemmatized_definition2	not astounding .
c.		concretize V concretization N concretiz ˆ (.+)e $ˆ (.+)ation$

Table 5: Examples of entries in the English defs.tsv table

a.		coffrage N décoffrer V coffr ˆ (.+)age $ˆ dé(.+)er$
		‘formwork’ ‘unmould’
	definition2	Retirer le coffrage après prise du béton.
		‘Remove the formwork after the concrete has set.’
	lemmatized_definition2	retirer le coffrage après prise de le béton .
b.		neutralité N neutraliser V neutral ˆ (.+)ité $ˆ (.+)iser$
		‘neutrality’ ‘neutralize’

Table 6: Examples of entries in the French defs.tsv table

	ordered pairs	defs	ratio	pair sets	defs	ratio
English	779,170	271,496	0.348	395,886	254,528	0.643
Russian	732,633	261,713	0.357	366,352	250,425	0.684
French	271,343	86,526	0.319	135,679	76,686	0.565
Polish	196,433	35,581	0.181	98,222	31,533	0.321
German	136,248	42,438	0.311	68,127	39,995	0.587
Italian	121,400	9,569	0.079	60,700	8,843	0.146

Table 7: Number of pairs of lexemes, with and without semantic descriptions, in the GLeMM lexicons

	correct		optimal
	pairs	accuracy	patterns	accuracy
English	94	0.94	87	0.93
French	99	0.99	92	0.93
Italian	100	1.00	84	0.84

Table 8: Accuracy of the pairs and optimality of the exponents

resources	languages	objects
CELEX	German, English, Dutch	complex lexemes
Démonette	French	relations (set-pairs)
Derivatario	Italian	complex lexemes
Derinet	Czech, Russian, Polish, Spanish	complex lexemes
DerivBase	German, Russian	first-level relations
MorphyNet	15 languages	relations (set-pairs)

Table 9: Objects of the existing resources considered for the quantitative comparison of GLeMM lexicons

Language	GLeMM	UDer	MorphyNet	UniMorph	CELEX	Other
English	395,886	32,483	225,131	225,131	52,447
Russian	366,352	303,615	93,039	14,048
French	135,679	13,808	72,952	73,259		111,059	(F)
Polish	98,222	214,093	58,711	58,742
German	68,127	43,367	29,381	29,381	51,728	63,479	(G)
Italian	60,700	1,783	58,848			11,147	(I)

Table 10: Size comparison between GLeMM and other resources

lexeme1	lexeme2	derivational type	MorphyNet	GLeMM
manage V	manager N	direct derivation	yes	yes
measurable ADJ	measurability N	direct derivation	yes	yes
characterize V	characterizability N	multiple derivations	yes	yes
hospitalize V	rehospitalization N	multiple derivations	no	yes
minimalist N	minimalism N	indirect (cross) relation	no	yes
productive ADJ	productible ADJ	indirect (cross) relation	no	yes

Table 11: Comparison of GLeMM ( pairs.tsv ) and MorphyNet for a few pairs of lexemes whose relations illustrate various derivational configurations.

	mutually motivated pairs	defined pairs	ratio
English	4,368	256,712	0.017
French	2,422	77,897	0.031
German	650	40,320	0.016
Italian	314	9,000	0.035
Polish	1,140	32,103	0.036
Russian	3,178	252,014	0.013

Table 12: Number of mutually motivated pairs in GLeMM lexicons

	transitive pairs	two-edge paths	ratio
English	10,573	179,290	0.059
French	4,557	40,288	0.113
German	404	17,305	0.023
Italian	175	1,987	0.088
Polish	857	11,977	0.072
Russian	8,508	206,200	0.041

Table 13: Number of transitive relations in the GLeMM lexicons

	cycles	two-edge paths	ratio
English	702	179,290	0.004
French	390	40,288	0.010
German	24	17,305	0.001
Italian	30	1,987	0.015
Polish	141	11,977	0.012
Russian	762	206,200	0.004

Table 14: Number of 3-edge cycles in GLeMM lexicons

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We present MorfFlex, a morphological dictionary architecture suitable for languages with extensive regularity in both inflection and derivation. As the primary example of MorfFlex in use we introduce MorfFlex CZ, a morphological dictionary of Czech. It is distributed as a simple, unstructured list of <wordform, lemma, tag> triplets, however, its manually maintained, unpublished source files and conversion scripts encode a sophisticated system of inflectional and derivational patterns. These patterns dramatically reduce the otherwise enormous size of the dictionary, which currently contains over 100 million wordforms and more than 1 million lemmas. The MorfFlex CZ dictionary serves as an essential resource for ensuring the consistency of manual morphological annotation in the Prague Dependency Treebanks and underpins state-of-the-art automatic tools such as MorphoDiTa. In this paper, we focus on: (i) presenting an effective method for managing the rich morphological system within the dictionary, and (ii) demonstrating the utility of such a language resource for maintaining annotation consistency in corpora and supporting the development of advanced NLP applications.

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