Document Structuring à la SDRT

In this paper, the issue of document structuring is addressed. To achieve this task, we advocate that Segmented Discourse Representation Theory (SDRT) is a most expressive discourse framework. Then we sketch a discourse planning mechanism which aims at producingas many paraphrastic document structures as possible from a set of factual data encoded into a logical form

Discourse Contribution of Enumerative Structures involving "pour deux raisons"

International audienceIn this article, we propose to study the discourse contribution of enumerative structures involving the prepositional phrase pour deux raisons. We would like to highlight the contribution of the textual information conveyed by enumerative structures and the prepositional phrase both to the discourse structure and the discourse content within the SDRT model. We will show that prepositional phrase like pour deux raisons must introduce a discourse constituent in the structure attached by the Commentary relation to the left context and the Enumeration relation to the right context. Finally we propose to treat pour deux raisons as a new kind of discourse marker: we will show that its discursive role within enumerative structures is to signal the content-level relation Explanation

A Rhetorical Structuring Model for Natural Language Generation in Human-Computer Multi-Party Dialogue

Multi-party human-computer dialogue research is still in its infancy. Most of the research in this respect either addresses dialogues between pairs of computers, or performs studies on multi-party human dialogue corpora, in order to better understand this type of interaction. Thus, there are only a few computational models for this type of linguistic interaction and this paper tries to fill this gap. However, only the issue of generating linguistically-appropriate speech turns in multi-party dialogue will be addressed here. For this, a formal framework that accounts for multi-party dialogue situations is developed. Then this model is customized, so that only the point of view of the machine is considered. Finally, several particularly interesting multi-party dialogue situations (for service-oriented systems) are enforced with algorithms for rhetorical structure updating for answer generation, in natural language, and evaluated on concrete multi-party dialogue examples

Supervision distante pour l'apprentissage de structures discursives dans les conversations multi-locuteurs

L'objectif principal de cette thèse est d'améliorer l'inférence automatique pour la modélisation et la compréhension des communications humaines. En particulier, le but est de faciliter considérablement l'analyse du discours afin d'implémenter, au niveau industriel, des outils d'aide à l'exploration des conversations. Il s'agit notamment de la production de résumés automatiques, de recommandations, de la détection des actes de dialogue, de l'identification des décisions, de la planification et des relations sémantiques entre les actes de dialogue afin de comprendre les dialogues. Dans les conversations à plusieurs locuteurs, il est important de comprendre non seulement le sens de l'énoncé d'un locuteur et à qui il s'adresse, mais aussi les relations sémantiques qui le lient aux autres énoncés de la conversation et qui donnent lieu à différents fils de discussion. Une réponse doit être reconnue comme une réponse à une question particulière ; un argument, comme un argument pour ou contre une proposition en cours de discussion ; un désaccord, comme l'expression d'un point de vue contrasté par rapport à une autre idée déjà exprimée. Malheureusement, les données de discours annotées à la main et de qualités sont coûteuses et prennent du temps, et nous sommes loin d'en avoir assez pour entraîner des modèles d'apprentissage automatique traditionnels, et encore moins des modèles d'apprentissage profond. Il est donc nécessaire de trouver un moyen plus efficace d'annoter en structures discursives de grands corpus de conversations multi-locuteurs, tels que les transcriptions de réunions ou les chats. Un autre problème est qu'aucune quantité de données ne sera suffisante pour permettre aux modèles d'apprentissage automatique d'apprendre les caractéristiques sémantiques des relations discursives sans l'aide d'un expert ; les données sont tout simplement trop rares. Les relations de longue distance, dans lesquelles un énoncé est sémantiquement connecté non pas à l'énoncé qui le précède immédiatement, mais à un autre énoncé plus antérieur/tôt dans la conversation, sont particulièrement difficiles et rares, bien que souvent centrales pour la compréhension. Notre objectif dans cette thèse a donc été non seulement de concevoir un modèle qui prédit la structure du discours pour une conversation multipartite sans nécessiter de grandes quantités de données annotées manuellement, mais aussi de développer une approche qui soit transparente et explicable afin qu'elle puisse être modifiée et améliorée par des experts.The main objective of this thesis is to improve the automatic capture of semantic information with the goal of modeling and understanding human communication. We have advanced the state of the art in discourse parsing, in particular in the retrieval of discourse structure from chat, in order to implement, at the industrial level, tools to help explore conversations. These include the production of automatic summaries, recommendations, dialogue acts detection, identification of decisions, planning and semantic relations between dialogue acts in order to understand dialogues. In multi-party conversations it is important to not only understand the meaning of a participant's utterance and to whom it is addressed, but also the semantic relations that tie it to other utterances in the conversation and give rise to different conversation threads. An answer must be recognized as an answer to a particular question; an argument, as an argument for or against a proposal under discussion; a disagreement, as the expression of a point of view contrasted with another idea already expressed. Unfortunately, capturing such information using traditional supervised machine learning methods from quality hand-annotated discourse data is costly and time-consuming, and we do not have nearly enough data to train these machine learning models, much less deep learning models. Another problem is that arguably, no amount of data will be sufficient for machine learning models to learn the semantic characteristics of discourse relations without some expert guidance; the data are simply too sparse. Long distance relations, in which an utterance is semantically connected not to the immediately preceding utterance, but to another utterance from further back in the conversation, are particularly difficult and rare, though often central to comprehension. It is therefore necessary to find a more efficient way to retrieve discourse structures from large corpora of multi-party conversations, such as meeting transcripts or chats. This is one goal this thesis achieves. In addition, we not only wanted to design a model that predicts discourse structure for multi-party conversation without requiring large amounts of hand-annotated data, but also to develop an approach that is transparent and explainable so that it can be modified and improved by experts. The method detailed in this thesis achieves this goal as well

EasyText : : un système opérationnel de génération de textes

International audienceThis paper introduces EasyText, a fully oper- ational NLG System. This application pro- cesses numerical data (in tables) in order to generate specific analytical comments of these tables.Cet article présente rapidement EasyText, un système opérationnel de génération de textes

CRPC-DB – A Discourse Bank for Portuguese

info:eu-repo/semantics/publishedVersio

An information-based approach to punctuation

Ankara : Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent University, 1998.Thesis (Ph. D.) -- Bilkent University, 1998.Includes bibliographical references leaves 83-93.Say, BilgePh.D