This article investigates the morphological diversity of agent nouns (ANs) in French. It addresses the questions of which nouns form a semantically coherent class of ANs, what their morphological properties are, and whether these properties correlate with agentive subtypes. To deal with these issues, a distributional semantics approach is adopted. The investigation is based on the distributional study of monosemous deverbal ANs ending in -eur, and on the examination of word similarities in the French Wikipedia corpus. It is shown that French ANs as a homogeneous distributional class display a large variety of morphological profiles. ANs can be affixed, converted, compound nouns, as well as opaque and morphologically simple nouns. Agentive affixes are diverse and correlated to the selection of bases from different lexical categories and semantic types. It appears that agent meaning in the nominal domain is not necessarily imported from the verbal domain, but can develop directly in the semantic structure of nouns. In addition, a distinction between functional, occasional and behavioral ANs, depending on whether they denote agents with an occupational status, agents in a particular event, or agents with a tendency to act in a certain way, is proven to be distributionally relevant. This distinction applies to all ANs, possibly in correlation with specificities as regards morphological type, base and affix selection. The study illustrates that with a careful processing of linguistic data, distributional semantics can help answering basic research questions and support fine-grained theoretical distinctions.

中文翻译：

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代理商里有什么？

本文研究了法语中座席名词（AN）的形态多样性。它解决了以下问题：哪些名词构成了语义上连贯的AN类，它们的形态学特性是什么，以及这些特性是否与代理子类型相关。为了解决这些问题，采用了分布式语义方法。这项调查基于对以-eur结尾的单反状语体AN的分布研究，以及对法国Wikipedia语料库中单词相似性的检查。结果表明，法国ANs作为同质分布类显示出各种各样的形态特征。AN可以附加，转换，复合名词，以及不透明和形态简单的名词。代理词缀是多种多样的，并且与从不同的词汇类别和语义类型中选择基数有关。看起来，名词域中的主体含义不一定从言语域中导入，而是可以直接在名词的语义结构中发展。另外，事实证明，功能性，偶然性和行为性AN之间的区别取决于它们是代表职业状态的代理商，在特定事件中的代理商还是倾向于以某种方式行动的代理商，这在分布上是相关的。这种区别适用于所有AN，可能与形态类型，碱基和词缀选择方面的特异性相关。该研究表明，通过对语言数据的仔细处理，