Corpus-based measures discriminate inflection and derivation cross-linguistically

In morphology, a distinction is commonly drawn between inflection and derivation. However, a precise definition of this distinction which reflects the way it manifests across languages remains elusive within linguistic theory, typically being based on subjective tests. In this study, we present 4 quantitative measures which use the statistics of a raw text corpus in a language to estimate to what extent a given morphological construction changes the form and distribution of lexemes. In particular, we measure both the average and the variance of this change across lexemes. Crucially, distributional information captures syntactic and semantic properties and can be operationalised by word embeddings. Based on a sample of 26 languages, we find that we can reconstruct 89±1% of the classification of constructions into inflection and derivation in UniMorph using our 4 measures, providing large-scale cross-linguistic evidence that the concepts of inflection and derivation are associated with measurable signatures in terms of form and distribution that behave consistently across a variety of languages. We also use our measures to identify in a quantitative way whether categories of inflection which have been considered noncanonical in the linguistic literature, such as inherent inflection or transpositions, appear so in terms of properties of their form and distribution. We find that while combining multiple measures reduces the amount of overlap between inflectional and derivational constructions, there are still many constructions near the model’s decision boundary between the two categories. This indicates a gradient, rather than categorical, distinction.

Authors

Institutions

Publication Details

Journal
Journal of Language Modelling
Published
2024-12-10
DOI
https://doi.org/10.15398/jlm.v12i2.351
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Corpus-based measures discriminate inflection and derivation cross-linguistically

Edoardo Maria Ponti, Coleman Haley, Sharon Goldwater
Journal of Language Modelling
Natural Language Processing Techniques
article

Corpus-based measures discriminate inflection and derivation cross-linguistically

Edoardo Maria Ponti, Coleman Haley, Sharon Goldwater
article en

Abstract

In morphology, a distinction is commonly drawn between inflection and derivation. However, a precise definition of this distinction which reflects the way it manifests across languages remains elusive within linguistic theory, typically being based on subjective tests. In this study, we present 4 quantitative measures which use the statistics of a raw text corpus in a language to estimate to what extent a given morphological construction changes the form and distribution of lexemes. In particular, we measure both the average and the variance of this change across lexemes. Crucially, distributional information captures syntactic and semantic properties and can be operationalised by word embeddings. Based on a sample of 26 languages, we find that we can reconstruct 89±1% of the classification of constructions into inflection and derivation in UniMorph using our 4 measures, providing large-scale cross-linguistic evidence that the concepts of inflection and derivation are associated with measurable signatures in terms of form and distribution that behave consistently across a variety of languages. We also use our measures to identify in a quantitative way whether categories of inflection which have been considered noncanonical in the linguistic literature, such as inherent inflection or transpositions, appear so in terms of properties of their form and distribution. We find that while combining multiple measures reduces the amount of overlap between inflectional and derivational constructions, there are still many constructions near the model’s decision boundary between the two categories. This indicates a gradient, rather than categorical, distinction.

Journal of Language ModellingVol. 12(2)
University of Edinburgh (GB)
Dell EMC, UK Research and Innovation, Engineering and Physical Sciences Research Council, Science and Technology Facilities Council
Reduced inequalities
Openalex Percentile: Top 79%
Natural Language Processing Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Corpus-based measures discriminate inflection and derivation cross-linguistically — Edoardo Maria Ponti, Coleman Haley, et al. · Journal of Language Modelling (2024) | TGRS Research Map | TGRS