Writing Tools: Looking Back to Look Ahead

Research on writing tools started with the increased availability of computers in the 1970s. After a first phase addressing the needs of programmers and data scientists, research in the late 1980s started to focus on writing-specific needs. Several projects aimed at supporting writers and letting them concentrate on the creative aspects of writing by having the writing tool take care of the mundane aspects using NLP techniques. Due to technical limitations at that time the projects failed and research in this area stopped. However, today's computing power and NLP resources make the ideas from these projects technically feasible; in fact, we see projects explicitly continuing from where abandoned projects stopped, and we see new applications integrating NLP resources without making references to those old projects. To design intelligent writing assistants with the possibilities offered by today's technology, we should re-examine the goals and lessons learned from previous projects to define the important dimensions to be considered.Comment: Final version of the position paper to participate in the Second Workshop on Intelligent and Interactive Writing Assistants (colocated with the ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) in Hamburg

SMM: Detailed, Structured Morphological Analysis for Spanish

We present a morphological analyzer for Spanish called SMM. SMM is implemented in the grammar development framework Malaga, which is based on the formalism of Left-Associative Grammar. We briefly present the Malaga framework, describe the implementation decisions for some interesting morphological phenomena of Spanish, and report on the evaluation results from the analysis of corpora. SMM was originally only designed for analyzing word forms; in this article we outline two approaches for using SMM and the facilities provided by Malaga to also generate verbal paradigms. SMM can also be embedded into applications by making use of the Malagaprogramming interface; we briefly discuss some application scenarios

Computational linguistics for word processing: opportunities and limits

In this paper the authors briefly outline editing functions which use methods from computational linguistics and take the structures of natural languages into consideration. Such functions could reduce errors and better support writers in realizing their communicative goals. However, linguistic methods have limits, and there are various aspects software developers have to take into account to avoid creating a solution looking for a problem: Language-aware functions could be powerful tools for writers, but writers must not be forced to adapt to their tools

Academic writing and publishing beyond documents

Research on writing tools stopped in the late 1980s when Microsoft Word had achieved monopoly status. However, the development of the Web and the advent of mobile devices are increasingly rendering static print-like documents obsolete. In this vision paper we reflect on the impact of this development on scholarly writing and publishing. Academic publications increasingly include dynamic elements, e.g., code, data plots, and other visualizations, which clearly requires other tools for document production than traditional word processors. When the printed page no longer is the desired final product, content and form can be addressed explicitly and separately, thus emphasizing the structure of texts rather than the structure of documents. The resulting challenges have not yet been fully addressed by document engineering

Large language models and artificial intelligence, the end of (language) learning as we know it—or not quite?

PreprintThe rapid advancements in large language models (LLM) and artificial intelligence (AI) have been a subject of recent significant interest and debate. This paper explores the impact of these developments on language learning. I discuss the technology underlying AI-based tools and the natural language processing (NLP) tasks they were originally designed for. This will help us to identify opportunities and limitations regarding their use in the context of language learning. I then examine how such technology can be used efficiently and effectively in language teaching and learning. The availability of such tools will require language teaching to focus on the non-mechanical aspects of writing. Similarly, automatically produced personalized teaching and learning materials will not replace human teachers, but give space for and support human–human interaction

Writing tools : looking back to look ahead

The organizers recommended publishing peer-reviewed, accepted, and revised papers on arXiv, no dedicated conference proceedings. This is the final version of this paper. Workshop website: https://in2writing.glitch.meResearch on writing tools started with the increased availability of computers in the 1970s. After a first phase addressing the needs of programmers and data scientists, research in the late 1980s started to focus on writing-specific needs. Several projects aimed at sup- porting writers and letting them concentrate on the creative aspects of writing by having the writing tool take care of the mundane aspects using NLP techniques. Due to technical limitations at that time the projects failed and research in this area stopped. However, today’s computing power and NLP resources make the ideas from these projects technically feasible; in fact, we see projects explicitly continuing from where abandoned projects stopped, and we see new applications integrating NLP resources without making references to those old projects. To design intelligent writing assistants with the possibilities offered by today’s technology, we should reexamine the goals and lessons learned from previous projects to define the important dimensions to be considered

Education as loosely coupled system of technology and pedagogy

Essay invited by the editors.The recent development of education seems to be driven mainly by technology; assigning version numbers is an attempt to tame this development. But education is more complex than buzzwords like “Learning 4.0” may suggest. In this article, we argue for viewing education as a loosely coupled system of two interacting layers, technology and pedagogy: closely connected, but not glued together. Using several examples, we show that sometimes technological innovations trigger pedagogical innovations and sometimes pedagogical needs initiate the development of technological solutions. We intend the model of loosely coupled layers of technology and pedagogy as a starting point for opening an overdue discussion on how to make the best use of technology for teaching and learning. We argue that complementing technology with established and proven principles of situated contextualized pedagogy is a key element for the future development of education

Neue Ansätze zur Auswertung von Schreibprozessdaten : Textgeschichten und Satzgeschichten

Begutachteter Abstract 3 Seiten, Posterpräsentatio

Digitale Diskursanalyse: Annotation und formale Modellierung von Diskursen

Dieser Beitrag widmet sich einer Auslegung des Begriffs digitale Diskursanalyse und fokussiert dabei auf den Aspekt des Digitalen. Wir argumentieren, dass Digitalität sich nicht auf Diskursmedium und -material oder verwendete Analysewerkzeuge bezieht, sondern für einen epistemologischen Ansatz steht, der es erlaubt, bislang eher vage und narrativ formulierte Elemente von Diskursen zu explizieren. So ist es möglich, Diskurse tatsächlich zu modellieren und empirisch fundierte Aussagen abzuleiten. Wir stellen den Prozess der Annotation von Diskursen auf verschiedenen Ebenen ins Zentrum und gelangen so zu einer adäquaten Sicht von digitaler Diskursanalyse als wissenschaftlich explizite und reproduzierbare Modellierung

Extraction of transforming sequences and sentence histories from writing process data : a first step towards linguistic modeling of writing

Online first, part of special issue "Methods for understanding writing process by analysis of writing timecourse" Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)Producing written texts is a non-linear process: in contrast to speech, writers are free to change already written text at any place at any point in time. Linguistic considerations are likely to play an important role, but so far, no linguistic models of the writing process exist. We present an approach for the analysis of writing processes with a focus on linguistic structures based on the novel concepts of transforming sequences, text history, and sentence history. The processing of raw keystroke logging data and the application of natural language processing tools allows for the extraction and filtering of product and process data to be stored in a hierarchical data structure. This structure is used to re-create and visualize the genesis and history for a text and its individual sentences. Focusing on sentences as primary building blocks of written language and full texts, we aim to complement established writing process analyses and, ultimately, to interpret writing timecourse data with respect to linguistic structures. To enable researchers to explore this view, we provide a fully functional implementation of our approach as an open-source software tool and visualizations of the results. We report on a small scale exploratory study in German where we used our tool. The results indicate both the feasibility of the approach and that writers actually revise on a linguistic level. The latter confirms the need for modeling written text production from the perspective of linguistic structures beyond the word level