À la poursuite de phrases: méthodes pour traiter des données dynamiques pour tracer la production de phrases (2025)

Abstract: Nous présentons des méthodes de traitement des données dynamiques permettant de retracer le processus de production de phrases. En tant qu’activité incrémentielle et non linéaire, l’écriture produit des versions intermédiaires incomplètes ou mal formées qui évoluent au fil de fréquentes révisions. À l’aide d’outils d’enregistrement des frappes et de traitement du langage naturel (TALN), nous proposons un cadre permettant de reconstruire automatiquement l’historique des phrases. De plus, nous implémentons dans THEtool un modèle qui synchronise l’historique des phrases avec les événements de révision et les patterns de pause. Cette représentation multicouche facilite la compréhension détaillée des aspects cognitifs et linguistiques de la construction des phrases.

## introduction keystroke logging is an established method to record writing processes. for each action performed by a writer, the relevant information on this event is stored: which key has been pressed and released at which point in time, its effect on the text, and the position of the cursor in the document. such process data is incremental, since events are executed in chronological order. however, it is not linear: writers can move back and forth in the text. two chronologically sequential events can take place at non-adjacent positions in the text. thus, writing is the incremental, nonlinear production of sentences. the text under production is often linguistically incomplete or ill-formed. additionally, sentence production is unpredictable: a syntactically complete sentence can be transformed back to an incomplete sentence or to a single letter at any point in time. it can be merged with an adjacent sentence or split into phrases. it can remain unfinished until the completion of the text, or it can be entirely deleted. some sentences might be produced without any revisions. to gain a comprehensive picture of the sentence production process, we also need to consider the time aspect. for a particular sentence, the creation may be interrupted by multiple pauses. only when keeping track of both the revisions and the pausing behavior, we can achieve a comprehensive understanding of the flow of sentence production. in this paper, we focus on methods to address challenges emerging from fully automatically handling writing process data characterized by incrementality, nonlinearity, contingency, incompleteness, and multidimensionality by leveraging existing natural language processing (nlp) tools. by developing new or refining existing concepts and definitions, and by creating specialized algorithms, we aim to provide insights into the intricate and multilayered nature of the sentence production process. ## related work understanding the dynamic nature of language production, particularly in writing, has gained increasing attention. the inherent dynamics of incremental events unfolding nonlinearly presents challenges for traditional linguistic models, which usually focus on the static final product. the analysis of keystroke logs has focused mostly on pauses as potential indicators of the underlying cognitive processes @xcite @xcite . pauses are often used to segment the writing stream into production units, so-called bursts @xcite . investigations of the syntactic structure of these bursts suggest that they often do not align neatly with traditional syntactic units @xcite and are often incomplete @xcite . however, keystroke logging data also enable the reconstruction of a text's evolution through intermediate versions determined by changes in production mode @xcite . more recent approaches focus on modeling the writing process by tracking changes across intermediate text versions @xcite . the concept of text history captures the sequence of all intermediate texts produced so far (tpsf). analyzing the transformations between these versions (transforming sequences) allows a detailed look at editing operations, as proposed by @xcite @xcite . the text history extraction tool (thetool) @xcite was developed to automate the extraction and analysis of text and sentence histories from keystroke logs, facilitating larger-scale studies into the dynamic, sentence-driven nature of writing. ## conclusion and outlook we have presented a comprehensive framework for analyzing keystroke logging data with a focus on sentence production. the concepts of sentence candidates, degree of sentencehood and text units introduced in @xcite and further developed in our recent work address incrementality, non-linearity, incompleteness and multidimensionality. our approach implemented in thetool, using existing nlp tools such as spacy and languagetool, enables automatic extraction and tracking of sentence histories from the writing process data and has been shown to provide reliable results @xcite . the introduction of a layer-based model supports a nuanced understanding of fundamental aspects of writing, and has been a starting point for the ongoing further development of thetool. future work will focus on further refining sentencehood criteria, enhancing the robustness of our approach, and extending thetool with syntactic parsing of spsfs. expanding the framework with further projections will allow us among others to investigate the syntactic structures within bursts.