SBE-UKRI: Understanding imprecise space and time in narratives through qualitative representations, reasoning, and visualisation

人类的经验主要以文本的形式记录和交流，而文本越来越多地以数字语料库的形式出现。社会科学、人文科学和计算机科学研究人员面临的一个主要挑战是如何在跨学科环境中使用这些文本，以发展对所描述经验的连贯理解。近年来，在地理信息科学（GISc）、语料库语言学、自然语言处理（NLP）、人文地理学、文学研究和数字人文学等领域，理解文本源中的地理信息已经引起了大量的研究兴趣。目前的技术水平涉及使用地理解析来自动识别文本中的地名并将其分配到坐标（Grover等人，2010年）。地名一旦以这种方式进行地理参照，就可以读入地理信息系统，用于制图和空间分析。还可以使用语料库语言学和NLP的技术进行分析，以查看哪些单词或主题与地名相关联，例如与情感反应相关联的地方，例如美丽或激发恐惧。这种方法的组合被称为地理文本分析（GTA）（Gregory et al 2015）。虽然GTA为理解语料库中的地理位置提供了一个有用的起点，但它是高度定量的，仅限于可以找到坐标的命名地点，并且几乎没有时间概念。然而，正如旅行的叙述非常清楚地表明的那样，人类对地理的体验往往是主观的，更适合于定性表征。在这些情况下，“地理”并不限于命名的地方;相反，它包含了模糊，不精确和模棱两可的地方，例如“营地”，“远处的山丘”或“沿着道路走得更远”，并包括使用“附近”，“在左边”或“几个小时的旅程”等术语的相对位置。这些定性表示是必要的上下文参考，但无法在地理空间技术（例如GIS）中进行管理。为了大规模地了解人类描述和与周围世界联系的方式，我们需要能够以联合收割机将所描述的空间体验的定性性质与使其可定量分析的方法相结合的方式，直观地表示和解释作者描述的地理。可视化，并分析定性和定量的参考地点和时间.这些方法将被应用到两个大型语料库的分析：一个语料库的旅行写作有关的英语湖区，主要写在18和19世纪;其他，大屠杀幸存者的证词语料库。虽然基于非常不同类型的旅行-休闲旅行和被迫迁移分别-这两个语料库代表了一个独特的声音，合并产生复杂的文化和经验的地理集合。该项目将探索NLP，语料库语言学，定性时空推理（QSTR），GISc和视觉分析的尖端数字技术如何帮助我们了解作者自己如何代表他们周围的地理，并探索这些文本所包含的地方感和经验的个体和聚合表示。由此产生的应用程序将对学术和非学术观众都具有重大意义。

Human experiences are recorded and communicated mostly as text which are increasingly available as digital corpora. A major challenge for researchers in the social sciences, humanities and computer sciences is how to use these texts in interdisciplinary settings to develop cohesive understandings of the experiences described. Understanding geographies in textual sources has received a significant amount of research interest in recent years across fields as diverse as geographical information science (GISc), corpus linguistics, natural language processing (NLP), human geography, literary studies, and digital humanities. The current state of the art involves using geoparsing to automatically identify the place names in texts and allocate them to a coordinate (Grover et al 2010). Once georeferenced in this way, place names can be read into a geographical information system for mapping and spatial analysis. Analysis can also be conducted using techniques from corpus linguistics and NLP to see what words or themes are associated with the place name such as the place being associated with emotional responses such as being beautiful or inspiring fear. This combination of approaches is known as geographical text analysis (GTA) (Gregory et al 2015). While GTA provides a useful starting point for understanding the geographies within a corpus, it is highly quantitative, is limited to named places for which coordinates can be found, and has little concept of time. Yet, as narratives of journeys make abundantly clear, human experiences of geography are more often subjective and more suited to qualitative representation. In these cases, "geography" is not limited to named places; rather, it incorporates the vague, imprecise, and ambiguous, with references to, for example, "the camp", "the hills in the distance", or "further down the road", and includes the relative locations using terms such as "near to", "on the left", or "a few hours' journey" from. These qualitative representations are necessary contextual referents but cannot be managed within geospatial technologies such as GIS. To understand on a large scale the ways in which humans describe and relate to the world around them, then, we need to be able to visually represent and interpret the geographies authors describe in ways that combine the qualitative nature of described spatial experiences with methods that render them quantitatively analysable.Drawing on a strongly interdisciplinary team, this grant will develop approaches that allow us to identify, extract, visualise, and analyse qualitative and quantitative references to place and time. These methods will be applied to analyses of two large corpora: one a corpus of travel writing about the English Lake District, predominantly written in the 18th and 19th centuries; the other, a corpus of Holocaust survivor testimonies. Although based on very different types of journey - leisure travel and forced migration respectively - both corpora represent a collection of unique voices that coalesce to generate complex cultural and experiential geographies. The project will explore how cutting-edge digital technologies from NLP, corpus linguistics, Qualitative Spatio-Temporal Reasoning (QSTR), GISc, and visual analytics can help us understand how authors themselves represented the geographies that surrounded them and explore the individual and aggregate representation of the sense and experience of place that these texts contain. The resulting applications will have great significance for scholarly and non-academic audiences alike.