NOMZ Nominalizations
Definition
Nouns derived from verbs or adjectives, typically ending in -tion, -ment, -ness, -ity (e.g., “investigation”, “development”, “happiness”).
Detection Rules
Nominalizations by suffix. Refines NN.
cql[word=".*tion|.*ment|.*ness|.*ity" & pos="NN|NNS"]
pybiber
pybiber matches nouns ending in -tion(s), -ment(s), -ness(es), -ity/-ities, then excludes a stoplist of words whose suffixes are coincidental (e.g., “city”, “apartment”, “moment”).
_base
cql[word=".*tions?|.*ments?|.*ness(es)?|.*it(y|ies)" & pos="NN|NNS"]
stop
→ word_list: stopcombine: _base & !stop
Words: stop (57 items)
apartment
apartments
attention
business
businesses
capacities
capacity
cities
city
comment
comments
condition
conditions
document
documents
edition
editions
element
elements
environment
environments
experiment
experiments
fiction
fictions
function
functions
humanity
identities
identity
mention
mentions
moment
moments
motion
motions
nation
nations
notion
notions
pity
position
positions
qualities
quality
section
sections
solution
solutions
station
stations
tradition
traditions
universities
university
witness
witnesses
Normalization
Per words
Sources
- biber_1988 — Biber, Douglas (1988) : Variation across Speech and Writing
- biber_2006 — Biber, Douglas (2006) : University Language — A Corpus-based Study of Spoken and Written Registers
- mfte — Le Foll, Elen & Shakir, Muhammad (2023/2025) : Multi-Feature Tagger of English (MFTE) — Python version
- pybiber — Brown, David West & Reinhart, Alex (2026) : pybiber — Python package for linguistic feature extraction and Multi-Dimensional Analysis
- xiao_2009 — Xiao, Richard (2009) : Multidimensional analysis and the study of world Englishes
- bohmann_2019 — Bohmann, Axel (2019) : Variation in English Worldwide: Varieties and Genres in a Quantitative Perspective
Notes
MFTE extended tagset includes NOMZ. Requires suffix-based heuristic or morphological analysis.