Towards Interactive Multidimensional Visualisations for Corpus Linguistics

Paul Rayson, J. Mariani, Bryce Anderson-Cooper, Alistair Baron, David Gullick, Andrew Moore, Stephen Wattam
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引用次数: 5

Abstract

We propose the novel application of dynamic and interactive visualisation techniques to support the iterative and exploratory investigations typical of the corpus linguistics methodology. Very large scale text analysis is already carried out in corpus-based language analysis by employing methods such as frequency profiling, keywords, concordancing, collocations and n-grams. However, at present only basic visualisation methods are utilised. In this paper, we describe case studies of multiple types of key word clouds, explorer tools for collocation networks, and compare network and language distance visualisations for online social networks. These are shown to fit better with the iterative data-driven corpus methodology, and permit some level of scalability to cope with ever increasing corpus size and complexity. In addition, they will allow corpus linguistic methods to be used more widely in the digital humanities and social sciences since the learning curve with visualisations is shallower for non-experts
面向语料库语言学的交互式多维可视化
我们提出动态和交互式可视化技术的新应用,以支持语料库语言学方法的迭代和探索性调查。在基于语料库的语言分析中,通过使用频率谱、关键词、一致性、搭配和n-grams等方法,已经进行了非常大规模的文本分析。然而,目前只使用基本的可视化方法。在本文中,我们描述了多种类型的关键词云,搭配网络的浏览器工具的案例研究,并比较了在线社交网络的网络和语言距离可视化。它们被证明更适合迭代数据驱动的语料库方法,并允许一定程度的可伸缩性来应对不断增加的语料库大小和复杂性。此外,它们将允许语料库语言学方法在数字人文和社会科学中得到更广泛的应用,因为对于非专家来说,可视化的学习曲线更浅
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