five

Blend recognizability in English as a foreign language. An experiment

收藏
DataCite Commons2023-01-24 更新2025-04-16 收录
下载链接:
http://siba-ese.unisalento.it/index.php/linguelinguaggi/article/view/26387/21908
下载链接
链接失效反馈
官方服务:
资源简介:
Recognisability is one of the major constraints that most linguists place on lexical blends and their well-formedness. Blends indeed display an unpredictable output that is not transparently analysable into morphemes, and their source words are difficult to recognise for both hearers and readers. The possible combinatory patterns of the source lexemes, the different portions that are retained in the final blend, and their semantic contribution to the overall meaning increase the number of variables and classificatory criteria for blends, thus decreasing predictability of the output given an input. For students of EFL, lexical blends are even more difficult to access due to the fact that the language in which they are formed is not their native language. This paper reports on results from an experiment on 18 Italian students who were tested on English blends. The participants were asked to identify the source words and meanings of a number of blends selected according to different (phonological, morphotactic, semantic) criteria. The results of the experiment show that the recognisability of English lexical blends by Italian native speakers depends on 1) the type of characteristics that the blend displays (overlap between the source words, semantic weight of the source words, headedness, same prosodic structure as one of the source words), 2) the category (substitution vs. overlap, coordinate vs. attributive) to which the blend belongs, and 3) the context where it is used. In general, the experiment sheds some light on the type of processes (e.g., decomposition and textual reference) involved in the recognition and accessibility of English lexical blends.
提供机构:
University of Salento
创建时间:
2023-01-24
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作