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<p>This paper investigates how different lexical factors influence inter-annotator agreement in a semantic annotation task, with the level of agreement serving as an indicator of task complexity. The study uses a dataset of approximately 5000 corpus instances of French nouns, each double annotated with supersenses representing broad semantic classes such as Person, Object and Event. Through statistical analysis, the study evaluates the individual impact of word ambiguity, frequency, concreteness and meaning hybridity on semantic classification. The results show that the four lexical factors under study are correlated with inter-annotator agreement and that they jointly predict a significant portion of the agreement data. More importantly, they contribute to a better understanding of how speakers categorise words semantically, while allowing for a comparison of manual and automated semantic classification based on lexical properties.</p>

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Exploring lexical factors in semantic annotation: insights from the classification of nouns in French

  • 期刊论文

  • 发表日期：

    2025年1月6日

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  • Lucie Barque,
  • Richard Huyghe,
  • Martial Foegel

摘要

This paper investigates how different lexical factors influence inter-annotator agreement in a semantic annotation task, with the level of agreement serving as an indicator of task complexity. The study uses a dataset of approximately 5000 corpus instances of French nouns, each double annotated with supersenses representing broad semantic classes such as Person, Object and Event. Through statistical analysis, the study evaluates the individual impact of word ambiguity, frequency, concreteness and meaning hybridity on semantic classification. The results show that the four lexical factors under study are correlated with inter-annotator agreement and that they jointly predict a significant portion of the agreement data. More importantly, they contribute to a better understanding of how speakers categorise words semantically, while allowing for a comparison of manual and automated semantic classification based on lexical properties.