Lexique et discours: Thèse d'HDR
Notice bibliographique
Résumé
In this document, I have summarized and put into perspective three research orientations I have been developing in the past fifteen years (2006-2022).The first one concerns the way spoken language is adapted in literary works, a technique used by a number of famous writers (Dickens, Molière, and many others). I was interested in the specific strategy of the French-Canadian author Michel Tremblay, who used phonetic, syntactic and lexical features of spoken French-Canadian language not only as a means of mirroring social differences within the French Quebec community but also as a source of genuine literary effects involving in particular metarepresentational ones. I studied five plays by Tremblay over a thirty year period (1968-1998), extracting the idiolectal statistical contrasts between socially symbolic characters and discussing them in the context of textual structure.My second field of research concerns discourse relations. In order to show that they are more than just a long list of terms for labeling relations between discourse segments, I chose three illustrative examples. (I) So-called paratactic constructions, where there is no lexical marking of discourse articulation, exhibit in some cases (pseudo-declaratives and pseudo-imperatives) interesting properties, such as non-compositionality or special prosodic patterns. (ii) I examine critically a particular symbolic/statistical treatment of argumentative relations in NLP, aiming at measuring the (non-)alignment of discourse and argumentative relations between discourse segments in short normalized texts. I point out that the results delivered by the algorithms are difficult to interpret and misrepresent the real geometry of argumentation in the texts. (iii) Finally I present some aspects of the evolution of comparative discourse markers over a period of six centuries, tracing back their path of change from comparative to causal and concessive values.My last area of interest concerns the semantic status of discourse markers. My general goal was to bring closer the large literature on such markers and the more general literature on information layering, most notably the approaches on presuppositions, implicatures and side issues in the sense of Gutzmann and Turgay (2019). I show that the broad category of discourse markers (DM) gathers those indexicals which are relevant to discourse interpretation, either with respect to its organization (structuring DM) or to the monitoring of the speaker’s “stance”, i.e. her emotional, attentional or belief state, her perception of events in the discourse situation and her interaction with other participants. As a result, I divided DM into two subcategories: connectives and hic and nunc particles (HNP). Connectives convey discourse relations between the semantic objects they refer, such as illocutionary acts, states of affairs, belief states, etc. They correspond to well-known cases like but, because or therefore. Certain connectives behave as presupposition triggers. HNP refer to external or internal (psychological) events, to other participants or to the speaker's speech itself (hesitations, corrections). They are partly analogous to expressives, in the sense of Potts (2005, 2007), but include non-expressive terms and are anchored to the utterance situation even more strongly than expressives. I also consider the combinations of DM, reporting on recent work using association measures and work in progress addressing jointly their semantic, prosodic and statistical properties. This kind of approach is developped in the CODIM project framework (Compositionality and Discourse Markers). This project, which I coordinate, is funded by the ANR (https://www.codim-project.org/).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,067 | 0,014 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».