We argue that, starting from a sophisticated understanding of "digital," we should develop a corresponding notion of "education" suitable to meet the challenges of the ongoing digital transformation. The central task for higher education institutions is to model the complex networks of digital skills (critical thinking, media literacy, cross-cultural competence, etc.) as a foundation for creating contextualized learning scenarios in the disciplines. The crucial success factor is the reunification of the classroom with the real world.
Writing requires a tool and a medium. Technology affords previously inconceivable ways of extending the functionalties of both. In this chapter, we focus on electronic writing tools, highlighting differences to older writing tools like pen and paper. We set the context by providing a brief history of word processing technology, before going on to survey the scope for tools that provide writing assistance to authors. We begin by surveying existing work on spell checking, grammar checking, and style checking, along with some specialist needs that can be met with existing tools. We then go on to look at the broader context of writing, exploring how state-ofthe-art natural language processing technologies might provide support for aspects of the writing process that, to date, have been out of reach for machines. We show the influence of input devices and word processor features on the writing process as well as on the resulting text. The chapter also covers aspects of fully automated text production, where writing is done by a machine. Writers and their toolsWriting is impossible without a tool and a medium: we write with a finger in the sand, with a pen on paper, with a spray can on a wall, or with a keyboard and intermediating software on a computer. Writers have to consider the topic and genre of what they are writing, take the audience into account, find the right words, and master their writing tool -all at the same time. Writing is also a task whose physical demands vary depending on the tools and media used: in former times, texts were produced on marble columns or clay tablets; using a typewriter from the 1980s is more exhausting than using a keyboard from the 21st century.The writer interacts with a tool and a medium to produce text. Given a class of writing tools -e.g., pen, typewriter, or keyboard and word processor -writers usually develop preferences. From the literature, we know about the writing habits of authors like Friedrich Nietzsche, Thomas Mann, Gabriel García Márquez, and Neal Stephenson, who relied on the use of a certain type of nib, typewriter, or word processor. The medium is also important: authors using a pen, a nib, or a typewriter not only prefer a certain type of the tool (including elements such as a specific ink or typewriter ribbon), but also a very specific type of paper. Others using word processors prefer certain types of screens or input devices. Authors may need a specific environment to produce text: Brought to you by |
Revising and editing are important parts of the writing process. In fact, multiple revision and editing cycles are crucial for the production of high-quality texts. However, revising and editing are also tedious and error-prone, since changes may introduce new errors. Grammar checkers, as offered by some word processors, are not a solution. Besides the fact that they are only available for few languages, and regardless of the questionable quality, their conceptual approach is not suitable for experienced writers, who actively create their texts. Word processors offer few, if any, functions for handling text on the same cognitive level as the author: While the author is thinking in high-level linguistic terms, editors and word processors mostly provide low-level character oriented functions. Mapping the intended outcome to these low-level operations is distracting for the author, who now has to focus for a long time on small parts of the text. This results in a loss of global overview of the text and in typical revision errors (duplicate verbs, extraneous conjunctions, etc.) We therefore propose functions for text processors that work on the conceptual level of writers. These functions operate on linguistic elements, not on lines and characters. We describe how these functions can be implemented by making use of NLP methods and linguistic resources. Abstract. Revising and editing are important parts of the writing process. In fact, multiple revision and editing cycles are crucial for the production of high-quality texts. However, revising and editing are also tedious and error-prone, since changes may introduce new errors. Grammar checkers, as offered by some word processors, are not a solution. Besides the fact that they are only available for few languages, and regardless of the questionable quality, their conceptual approach is not suitable for experienced writers, who actively create their texts. Word processors offer few, if any, functions for handling text on the same cognitive level as the author: While the author is thinking in high-level linguistic terms, editors and word processors mostly provide low-level character oriented functions. Mapping the intended outcome to these low-level operations is distracting for the author, who now has to focus for a long time on small parts of the text. This results in a loss of global overview of the text and in typical revision errors (duplicate verbs, extraneous conjunctions, etc.). We therefore propose functions for text processors that work on the conceptual level of writers. These functions operate on linguistic elements, not on lines and characters. We describe how these functions can be implemented by making use of NLP methods and linguistic resources. Linguistic Support for Revising and Editing
A key difference between traditional humanities research and the emerging field of digital humanities is that the latter aims to complement qualitative methods with quantitative data. In linguistics, this means the use of large corpora of text, which are usually annotated automatically using natural language processing tools. However, these tools do not exist for historical texts, so scholars have to work with unannotated data. We have developed a system for systematic, iterative exploration and annotation of historical text corpora, which relies on an XML database (BaseX) and in particular on the Full Text and Update facilities of XQuery.
Unlike programmers, authors only get very little support from their writing tools, i.e., their word processors and editors. Current editors are unaware of the objects and structures of natural languages and only offer character-based operations for manipulating text. Writers thus have to execute complex sequences of low-level functions to achieve their rhetoric or stylistic goals while composing. Software requiring long and complex sequences of operations causes users to make slips. In the case of editing and revising, these slips result in typical revision errors, such as sentences without a verb, agreement errors, or incorrect word order. In the LingURed project, we are developing language-aware editing functions to prevent errors. These functions operate on linguistic elements, not characters, thus shortening the command sequences writers have to execute. This paper describes the motivation and background of the LingURed project and shows some prototypical language-aware functions. Linguistic Editing Support ABSTRACTUnlike programmers, authors only get very little support from their writing tools, i.e., their word processors and editors. Current editors are unaware of the objects and structures of natural languages and only offer character-based operations for manipulating text. Writers thus have to execute complex sequences of low-level functions to achieve their rhetoric or stylistic goals while composing. Software requiring long and complex sequences of operations causes users to make slips. In the case of editing and revising, these slips result in typical revision errors, such as sentences without a verb, agreement errors, or incorrect word order. In the LingURed project, we are developing language-aware editing functions to prevent errors. These functions operate on linguistic elements, not characters, thus shortening the command sequences writers have to execute. This paper describes the motivation and background of the LingURed project and shows some prototypical language-aware functions.
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