nominalizator

Nominalizator, in linguistic and computational contexts, refers to a process, tool, or component that creates nominalizations—forms that turn verbs or adjectives into nouns. The term is closely related to nominalization, a common grammatical phenomenon in which action or attribute words are converted into noun forms. A nominalizator may be described as a mechanism (human, algorithmic, or theoretical) that produces, analyzes, or highlights such nominalized forms.

In practice, nominalizators operate through various methods. Traditional, rule-based systems apply predefined suffixes or conversion rules to generate nouns from verbs (for example, investigate → investigation, decide → decision, observe → observation) or to transform adjectives into related nouns (happy → happiness, efficient → efficiency). In multilingual or machine learning contexts, statistical models or neural networks predict nominalizations from input text, optionally using dictionaries or morpho-syntactic features. Some tools focus on detection—flagging potential nominalizations for editing to improve readability—and others perform transformation to adjust style, often reducing excessive nominalization in favor of more direct verb phrases.

Applications span linguistics, natural language processing, and writing assistance. Researchers study nominalization to understand cognitive processing, information packaging, and text cohesion. In editing and readability workflows, nominalizators help assess or modify dense prose, since high nominalization can obscure agency and action. Limitations include irregular or language-specific patterns, context sensitivity, and the potential to alter nuance when transforming forms. Overall, nominalizator serves as a useful concept and tool for analyzing and managing nominalization in text.