作为CLDF数据集的南岛语和密克罗尼西亚语比较词典。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Alexander D Smith, Robert Forkel, Lev Blumenfeld
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引用次数: 0

摘要

《南岛语比较词典》自1990年Robert Blust开始编纂以来,一直是南岛语比较研究的重要资源。同样,密克罗尼西亚比较词典——拜伦·本德和他的同事先前在《海洋语言学》上发表的密克罗尼西亚原始重建的在线数据库——是比较语言学的一个重要参考点。遗留下来的是,这两本词典的在线版本都有一个不确定的未来,而且都没有一个结构化的格式,适合定量方法。因此,为了为科学记录保留两个字典的内容并增加数据的互操作性,我们将字典转换为CLDF数据集。虽然对每个字典中的数据的编程访问已经提供了一个新的可用性级别,但CLDF中数据的真正潜力在于跨数据集的互操作性。这对这里介绍的两本词典特别有用,因为密克罗尼西亚语属于南岛语族,所以密克罗尼西亚的数据可能会补充南岛语族比较词典。有了CLDF数据集,我们为解决这一挑战奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The Austronesian and the Micronesian Comparative Dictionaries as CLDF datasets.

The Austronesian and the Micronesian Comparative Dictionaries as CLDF datasets.

The Austronesian and the Micronesian Comparative Dictionaries as CLDF datasets.

The Austronesian and the Micronesian Comparative Dictionaries as CLDF datasets.

The Austronesian Comparative Dictionary has served as an important resource for the comparative study of Austronesian languages since Robert Blust started its compilation in 1990. Likewise, the Micronesian Comparative Dictionary - an online database of Proto-Micronesian Reconstructions previously published in Oceanic Linguistics by Byron Bender and colleagues - is an important reference point for comparative Linguistics. The legacy, online versions of both dictionaries share an uncertain future, and both have not been available in a structured format, amenable to quantitative methods. Thus, to preserve the content of both dictionaries for the scientific record and to increase interoperability of the data, we undertook a conversion of the dictionaries to CLDF datasets. While programmatic access to the data within each dictionary already provides a new level of usability, the true potential of data in CLDF lies in interoperability across datasets. This is particularly useful for the two dictionaries presented here, because Micronesian languages belong to the Austronesian family and so the Micronesian data could potentially complement the Austronesian Comparative Dictionary. With the CLDF datasets we lay the groundwork for tackling this challenge.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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