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Revised Typology of Place-Naming 修订地名类型学
Pub Date : 2021-11-15 DOI: 10.5195/names.2021.2260
David Blair,Jan Tent
A functional and systematic typology of toponyms is an essential instrument for the toponymist wishing to investigate the naming practices and patterns of a region. To this end, the Australian National Placenames Survey developed a toponym typology for Australia (Tent & Blair 2011). This was characterized as a ‘typology of motivations for naming’. Although various researchers have used this typology with seeming success, further application of the typology to the Survey’s database of toponyms has revealed the need for a re-evaluation of the naming process. This occasioned a modification of some toponym categories generating a revised typology which can be considered a ‘typology of expressions of the naming intention’.
一个功能和系统的地名类型学是一个重要的工具,为地名学者希望调查一个地区的命名实践和模式。为此,澳大利亚国家地名调查制定了澳大利亚的地名类型学(Tent &布莱尔2011年)。这被称为“命名动机的类型学”。尽管不同的研究人员已经成功地使用了这种类型学,但进一步将这种类型学应用于调查的地名数据库表明,需要重新评估命名过程。这引起了一些地名类别的修改,产生了一种修订的类型学,可以被认为是“命名意图表达的类型学”。
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引用次数: 6
“Boundary-Maintenance” or “Boundary-Crossing”? Name-Giving Practices among Immigrants in Germany “维持边界”还是“跨越边界”?德国移民的取名习惯
Pub Date : 2021-08-16 DOI: 10.5195/names.2021.2237
Jurgen Gerhards,Julia Tuppat
This study investigates why some immigrants choose names for their children that are common in their home country whereas others opt for names used by natives in the host country. Drawing on the sociological literature on symbolic boundaries, the first strategy can be described as boundary-maintenance whereas the second can be classified as boundary-crossing. Using data from the German Socio-Economic Panel Study and applying bivariate and multivariate methods, two broader explanations for name-giving practices are tested: (1) cultural proximity and the permeability of the symbolic boundary between home and host country; and (2) immigrants’ levels of linguistic, structural, social, and emotional integration in the host country. Overall, the theoretical model explains the differences very satisfactorily. Whilst both sets of factors proved relevant to immigrants’ name-giving practices, the immigrants’ level of integration in the host country was less important than the cultural proximity between the origin group and host country.
这项研究调查了为什么一些移民为他们的孩子选择在母国常见的名字,而另一些移民则选择东道国当地人使用的名字。借鉴社会学关于符号边界的文献,第一种策略可被描述为维持边界,而第二种策略可被归类为跨越边界。利用德国社会经济小组研究的数据,并应用双变量和多变量方法,对起名行为的两种更广泛的解释进行了检验:(1)文化邻近性和母国与东道国之间符号边界的渗透性;(2)移民在东道国的语言、结构、社会和情感融合水平。总的来说,理论模型非常令人满意地解释了这些差异。虽然这两组因素都被证明与移民的取名行为有关,但移民在东道国的融入程度不如原籍群体与东道国之间的文化接近程度重要。
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引用次数: 0
Corpus-Based Methods for Recognizing the Gender of Anthroponyms 基于语料库的拟人词性别识别方法
Pub Date : 2021-08-16 DOI: 10.5195/names.2021.2238
Rogelio Nazar,Irene Renau,Nicolas Acosta,Hernan Robledo,Maha Soliman,Sofıa Zamora
This paper presents a series of methods for automatically determining the gender of proper names, based on their co-occurrence with words and grammatical features in a large corpus. Although the results obtained were for Spanish given names, the method presented here can be easily replicated and used for names in other languages. Most methods reported in the literature use pre-existing lists of first names that require costly manual processing and tend to become quickly outdated. Instead, we propose using corpora. Doing so offers the possibility of obtaining real and up-to-date name-gender links. To test the effectiveness of our method, we explored various machine-learning methods as well as another method based on simple frequency of co-occurrence. The latter produced the best results: 93% precision and 88% recall on a database of ca. 10,000 mixed names. Our method can be applied to a variety of natural language processing tasks such as information extraction, machine translation, anaphora resolution or large-scale delivery or email correspondence, among others.
本文提出了一套基于大语料库中专名与词的共现性和语法特征自动确定专名性别的方法。虽然得到的结果是针对西班牙语的名字,但这里介绍的方法可以很容易地复制并用于其他语言的名字。文献中报道的大多数方法使用预先存在的名字列表,这需要昂贵的人工处理,而且往往很快就会过时。相反,我们建议使用语料库。这样做提供了获得真实和最新的姓名-性别链接的可能性。为了测试我们方法的有效性,我们探索了各种机器学习方法以及另一种基于简单共现频率的方法。后者产生了最好的结果:在大约10,000个混合名字的数据库中,准确率为93%,召回率为88%。我们的方法可以应用于各种自然语言处理任务,如信息提取、机器翻译、回指解析或大规模交付或电子邮件通信等。
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引用次数: 0
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