Degree modifiers represent linguistic items employed to alter other elements in relation to their degree. Despite being a well-studied category in English linguistics, degree modifiers in Croatian have received limited attention. This study aims to address this gap by examining a set of Croatian degree modifiers as a part of construction. Initially, a corpus analysis is used, and the 29 most frequent degree modifiers of adjectives in the hrWaC corpus are identified. To analyse the examined modifiers, we turn to the distributional hypothesis and examine collocational contexts in which modifiers occur. By employing a simple collexeme analysis, we quantify the degree of attraction between a given degree modifier and adjective for each construction and its 1000 most frequent adjectival collocates. The results of simple collexeme analysis then serve as input for hierarchical agglomerative cluster analysis, shedding light on the clustering patterns of Croatian degree modifi ers based on their favoured collexemes. Simple collexeme analysis reveals itself as successful in filtering out collexems that consistently appear irrespective of the context, proving its superiority over methods relying solely on raw frequencies. The subsequent cluster analysis exposes some discrepancies between the modifiers' function and their cluster profiling, resulting in clusters lacking functional homogeneity. Nonetheless, certain subclusters demonstrate perfect or almost perfect stability and empirical support, affirming the (near-)synonymy among involved modifiers.

Ivan Lacić (2024). An insight into the Croatian degree modifier paradigm and its clustering profiles. SUVREMENA LINGVISTIKA, 50(97), 85-112 [10.22210/suvlin.2024.097.04].

An insight into the Croatian degree modifier paradigm and its clustering profiles

Ivan Lacić
2024

Abstract

Degree modifiers represent linguistic items employed to alter other elements in relation to their degree. Despite being a well-studied category in English linguistics, degree modifiers in Croatian have received limited attention. This study aims to address this gap by examining a set of Croatian degree modifiers as a part of construction. Initially, a corpus analysis is used, and the 29 most frequent degree modifiers of adjectives in the hrWaC corpus are identified. To analyse the examined modifiers, we turn to the distributional hypothesis and examine collocational contexts in which modifiers occur. By employing a simple collexeme analysis, we quantify the degree of attraction between a given degree modifier and adjective for each construction and its 1000 most frequent adjectival collocates. The results of simple collexeme analysis then serve as input for hierarchical agglomerative cluster analysis, shedding light on the clustering patterns of Croatian degree modifi ers based on their favoured collexemes. Simple collexeme analysis reveals itself as successful in filtering out collexems that consistently appear irrespective of the context, proving its superiority over methods relying solely on raw frequencies. The subsequent cluster analysis exposes some discrepancies between the modifiers' function and their cluster profiling, resulting in clusters lacking functional homogeneity. Nonetheless, certain subclusters demonstrate perfect or almost perfect stability and empirical support, affirming the (near-)synonymy among involved modifiers.
2024
Ivan Lacić (2024). An insight into the Croatian degree modifier paradigm and its clustering profiles. SUVREMENA LINGVISTIKA, 50(97), 85-112 [10.22210/suvlin.2024.097.04].
Ivan Lacić
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/978916
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact