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Towards a web-based platform for plotting, visualizing and enriching diachronic semantic maps: With a case study on the Greek and Egyptian temporal semantic field

机译:建立基于Web的平台以绘制,可视化和丰富历时语义图:以希腊和埃及时态语义领域为例

摘要

Semantic maps aim at detecting cross-linguistic regularities and recurrent patterns in semantic structure (Haspelmath 2003). This method was initially applied to the grammatical domain, mostly in a synchronic perspective (with some exceptions, see, e.g., van der Auwera & Plungian 1998). Recent research, however, has drawn attention to the lexical domain, showing that the model can also include lexical semantics (see, e.g., François 2008).Intimately related to the semantic map method has been the issue of finding appropriate ways of creating those maps and of capturing visually the semantic regularities. Up until recently, the maps were plotted and drawn manually. However, Regier et al. (2013) showed that a good approximation algorithm exists for inferring semantic maps based on polysemy data. Elaborating on their method, this paper aims to demonstrate that information about the directionality and the weight of the edges can be automatically added, thereby providing valuable information regarding the paths of semantic extensions and their frequency. In order to illustrate the method, we take the example of the semantic extension of time-related lexemes (e.g. TIME, HOUR, SEASON, DAY) in Ancient Greek (8th – 1st c. BC) and Ancient Egyptian – Coptic (26th c. BC – 10th c. AD). Both languages give access to significant diachronic material, allowing us to trace long term processes of semantic change. The results of our diachronic investigations are then checked against databases giving information on synchronic polysemies (e.g., List et al. 2014). In doing so, we also assess the adequacy of the use of polysemy as a tool to investigate semantic change.
机译:语义图旨在检测语义结构中的跨语言规律和递归模式(Haspelmath 2003)。这种方法最初是在语法领域中应用的,主要是从同步角度考虑的(有些例外,例如参见van der Auwera&Plungian 1998)。然而,最近的研究引起了对词法领域的关注,表明该模型还可以包括词法语义(例如,参见François2008)。与语义图方法密切相关的问题是找到创建这些图的合适方法的问题。并从视觉上捕捉语义规律。直到最近,这些地图都是手动绘制和绘制的。但是,Regier等。 (2013年)表明,存在一个很好的近似算法,用于基于多义数据推断语义图。详细介绍它们的方法,本文旨在证明可以自动添加有关方向性和边缘权重的信息,从而提供有关语义扩展路径及其频率的有价值的信息。为了说明该方法,我们以与时间相关的词素(例如,TIME,HOUR,SEASON,DAY)在古希腊语(公元前8至1世纪)和古埃及语-科普特语(26世纪)中进行语义扩展为例。公元前–公元10世纪)。两种语言都可以访问重要的历时资料,从而使我们能够追踪语义变化的长期过程。然后根据提供共时性一词多义信息的数据库检查我们历时调查的结果(例如List等人2014)。在此过程中,我们还将评估使用多义词作为调查语义变化的工具的适当性。

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