This paper presents the methodology, design principles and detailed evaluation of a new freely available multilayer corpus, collected and edited via classroom annotation using collaborative software. After briefly discussing corpus design for open, extensible corpora, five classroom annotation projects are presented, covering structural markup in TEI XML, multiple part of speech tagging, constituent and dependency parsing, information structural and coreference annotation, and Rhetorical Structure Theory analysis. Layers are inspected for annotation quality and together they coalesce to form a richly annotated corpus that can be used to study the interactions between different levels of linguistic description. The evaluation gives an indication of the expected quality of a corpus created by students with relatively little training. A multifactorial example study on lexical NP coreference likelihood is also presented, which illustrates some applications of the corpus. The results of this project show that high quality, richly annotated resources can be created effectively as part of a linguistics curriculum, opening new possibilities not just for research, but also for corpora in linguistics pedagogy.

The GUM corpus: creating multilayer resources in the classroom

We present an overview of the last ten years of research on visualizations that support close and distant reading of textual data in the digital humanities. We look at various works published within both the visualization and digital humanities communities. We provide a taxonomy of applied methods for close and distant reading, and illustrate approaches that combine both reading techniques to provide a multifaceted view of the data. Furthermore, we list toolkits and potentially beneficial visualization approaches for research in the digital humanities. Finally, we summarize collaboration experiences when developing visualizations for close and distant reading, and give an outlook on future challenges in that research area.

On Close and Distant Reading in Digital Humanities: A Survey and Future Challenges

This article is concerned with the data structures, properties of query languages, and visualization facilities required for the generic representation of richly annotated, heterogeneous linguistic corpora. We propose that above and beyond a general graph-based data model, which is becoming increasingly popular in many complex annotation formats, a well-defined concept of multiple, potentially conflicting segmentation layers must be introduced to deal with different sources and applications of corpus data flexibly. We also propose a generic solution for specialized corpus visualizations in a Web interface using annotation-triggered style sheets, which leverage the power of modern browsers and CSS for multiple and highly customizable views of primary data. We offer an implementation and evaluation of our architecture in ANNIS3, an open-source browser-based architecture for corpus search and visualization. We present three case studies to test the coverage of the system, encompassing core linguistic and digital humanities use-cases including richly annotated newspaper treebanks, multilingual diplomatic and normalized manuscript materials edited in TEI, and analysis of multimodal recordings of spoken language.

https://corpling.uis.georgetown.edu/amir/pdf/ANNIS3_DSH_prepub.pdf

ANNIS3: A new architecture for generic corpus query and visualization

In 2005, Franco Moretti introduced Distant Reading to analyse entire literary text collections. This was a rather revolutionary idea compared to the traditional Close Reading, which focuses on the thorough interpretation of an individual work. Both reading techniques are the prior means of Visual Text Analysis. We present an overview of the research conducted since 2005 on supporting text analysis tasks with close and distant reading visualizations in the digital humanities. Therefore, we classify the observed papers according to a taxonomy of text analysis tasks, categorize applied close and distant reading techniques to support the investigation of these tasks and illustrate approaches that combine both reading techniques in order to provide a multi-faceted view of the textual data. In addition, we take a look at the used text sources and at the typical data transformation steps required for the proposed visualizations. Finally, we summarize collaboration experiences when developing visualizations for close and distant reading, and we give an outlook on future challenges in that research area.

Visual Text Analysis in Digital Humanities

Swiss dialects of German are, unlike most dialects of well standardised languages, widely used in everyday communication. Despite this fact, automatic processing of Swiss German is still a considerable challenge due to the fact that it is mostly a spoken variety rarely recorded and that it is subject to considerable regional variation. This paper presents a freely available general-purpose corpus of spoken Swiss German suitable for linguistic research, but also for training automatic tools. The corpus is a result of a long design process, intensive manual work and specially adapted computational processing. We first describe how the documents were transcribed, segmented and aligned with the sound source, and how inconsistent transcriptions were unified through an additional normalisation layer. We then present a bootstrapping approach to automatic normalisation using different machine-translation-inspired methods. Furthermore, we evaluate the performance of part-of-speech taggers on our data and show how the same bootstrapping approach improves part-of-speech tagging by 10% over four rounds. Finally, we present the modalities of access of the corpus as well as the data format.

/pdf/archimob-a-corpus-of-spoken-swiss-german-2lcmxx74g9.pdf

ArchiMob - A Corpus of Spoken Swiss German

This paper introduces a multi-layer corpus architecture with multiple tokenizations using the open source historical, diachronic corpus of German called Register in Diachronic German Science. The corpus contains herbal texts printed between the fifteenth and nineteenth centuries and is concerned with the development of a German scientific register, independent of Latin. We will discuss difficulties of transcribing, normalizing and annotating historical texts and will thereby argue for the advantages of multiple layers and multiple tokenizations. A virtually infinite number of annotations can be added to the corpus, without the need for deciding between or discarding interpretations. Thus, this flexible architecture enables multiple normalizations and types of annotation and is open to a wide range of research questions in the humanities. We provide case studies concerning the exploitation of our different normalizations as well as structural, register-specific and linguistic annotations. The corpus architecture allows for its reuse as a resource for corpus-based research approaches.

RIDGES Herbology: designing a diachronic multi-layer corpus

This paper introduces an open source, interoperable generic software tool set catering for the entire workflow of creation, migration, annotation, query and analysis of multi-layer linguistic corpora. It consists of four components: Salt, a graph-based meta model and API for linguistic data, the common data model for the rest of the tool set; Pepper, a conversion tool and platform for linguistic data that can be used to convert many different linguistic formats into each other; Atomic, an extensible, platform-independent multi-layer desktop annotation software for linguistic corpora; ANNIS, a search and visualization architecture for multi-layer linguistic corpora with many different visualizations and a powerful native query language. The set was designed to solve the following issues in a multi-layer corpus workflow: Lossless data transition between tools through a common data model generic enough to allow for a potentially unlimited number of different types of annotation, conversion capabilities for different linguistic formats to cater for the processing of data from different sources and/or with existing annotations, a high level of extensibility to enhance the sustainability of the whole tool set, analysis capabilities encompassing corpus and annotation query alongside multi-faceted visualizations of all annotation layers.

corpus-tools.org: An Interoperable Generic Software Tool Set for Multi-layer Linguistic Corpora.

We present graphANNIS, a fast implementation of the established query language AQL for dealing with deeply annotated linguistic corpora. AQL builds on a graphbased abstraction for modeling and exchanging linguistic data, yet all its current implementations use relational databases as storage layer. In contrast, graphANNIS directly implements the ANNIS graph data model in main memory. We show that the vast majority of the AQL functionality can be mapped to the basic operation of finding paths in a graph and present efficient implementations and index structures for this and all other required operations. We compare the performance of graphANNIS with that of the standard SQL-based implementation of AQL, using a workload of more than 3000 real-life queries on a set of 17 open corpora each with a size up to 3 Million tokens, whose annotations range from simple and linear part-of-speech tagging to deeply nested discourse structures. For the entire workload, graphANNIS is more than 40 times faster, and slower in less than 3% of the queries. graphANNIS as well as the workload and corpora used for evaluation are freely available at GitHub and the Zenodo Open Access archive.

graphANNIS: A Fast Query Engine for Deeply Annotated Linguistic Corpora.

Genetics plays an ever-increasing role in medical diagnostics. The requirements for laboratory diagnostics are constantly changing due to new emerging diagnostic procedures, methodologies, devices, and regulatory requirements. Standard software already available for laboratories often cannot keep up with the latest developments or is focused on research rather than process automation. Although the software utilized in diagnostic laboratories is subject to regulatory requirements, there is no well-defined formal procedure for software development. Reference models have been developed to formalize these solutions, but they do not facilitate the initial requirements analysis or the development process itself. A systematic requirements engineering process is however not only essential to ensure the quality of the final product but is also required by regulations such as the European In Vitro Diagnostic Regulation and international standards such as IEC 62304. This paper shows, by example, the systematic requirements analysis of a system for qPCR-based (quantitative polymerase chain reaction) gene expression analysis. Towards this goal, a multi-step research approach was employed, which included literature review, user interviews, and market analysis. Results revealed the complexity of the field with many requirements to be considered for future implementation.

https://www.mdpi.com/2306-5354/9/4/144/pdf?version=1648537285

Thomas Krause

Papers

ANNIS3: A new architecture for generic corpus query and visualization

RIDGES Herbology: designing a diachronic multi-layer corpus

corpus-tools.org: An Interoperable Generic Software Tool Set for Multi-layer Linguistic Corpora.

graphANNIS: A Fast Query Engine for Deeply Annotated Linguistic Corpora.

A Systematic Approach to Diagnostic Laboratory Software Requirements Analysis