Text as scene: discourse deixis and bridging relations

En este artículo se presenta un nuevo marco, “el texto como escena”, que establece las bases para la anotación de dos relaciones de correferencia: la deixis discursiva y las relaciones de bridging. La incorporación de lo que llamamos escenas textuales y contextuales proporciona unas directrices de anotación más flexibles, que diferencian claramente entre tipos de categorías generales. Un marco como éste, capaz de tratar la deixis discursiva y las relaciones de bridging desde una perspectiva común, tiene como objetivo mejorar el bajo grado de acuerdo entre anotadores obtenido por esquemas de anotación anteriores, que son incapaces de captar las referencias vagas inherentes a estos dos tipos de relaciones. Las directrices aquí presentadas completan el esquema de anotación diseñado para enriquecer el corpus español CESS-ECE con información correferencial y así construir el corpus CESS-Ancora.This paper presents a new framework, “text as scene”, which lays the foundations for the annotation of two coreferential links: discourse deixis and bridging relations. The incorporation of what we call textual and contextual scenes provides more flexible annotation guidelines, broad type categories being clearly differentiated. Such a framework that is capable of dealing with discourse deixis and bridging relations from a common perspective aims at improving the poor reliability scores obtained by previous annotation schemes, which fail to capture the vague references inherent in both these links. The guidelines presented here complete the annotation scheme designed to enrich the Spanish CESS-ECE corpus with coreference information, thus building the CESS-Ancora corpus.This paper has been supported by the FPU grant (AP2006-00994) from the Spanish Ministry of Education and Science. It is based on work supported by the CESS-ECE (HUM2004-21127), Lang2World (TIN2006- 15265-C06-06), and Praxem (HUM2006- 27378-E) projects

Linguistic Markers of Lexical and Textual Relations in Technical Documents

International audienceThis chapter proposes a number of linguistic " handles " for the description of technical documents, at a lexical level (terminology) and at a textual level (discourse coherence). Examples are given of uses of such insights in document production and management, in particular via document engineering systems. We provide a number of linguistic " handles " for the description of technical documents. Such insights into the " inner workings " of texts may be harnessed in various ways in the production and management of technical documents; we show some applications in document engineering, in systems designed to facilitate access to information. Our focus is on surface markers, i.e. observable text features identified through corpus analysis, signalling the kind of relations between lexical items used in building terminologies (such as generic/specific, see section 1), or relations between text segments involved in discourse coherence (such as theme, or rhetorical relations, see section 2). We insist on the relevance of the notion of genre when working with technical documents, and on the genre-dependent nature of our linguistic markers

Construction Grammar and Language Models

Recent progress in deep learning and natural language processing has given rise to powerful models that are primarily trained on a cloze-like task and show some evidence of having access to substantial linguistic information, including some constructional knowledge. This groundbreaking discovery presents an exciting opportunity for a synergistic relationship between computational methods and Construction Grammar research. In this chapter, we explore three distinct approaches to the interplay between computational methods and Construction Grammar: (i) computational methods for text analysis, (ii) computational Construction Grammar, and (iii) deep learning models, with a particular focus on language models. We touch upon the first two approaches as a contextual foundation for the use of computational methods before providing an accessible, yet comprehensive overview of deep learning models, which also addresses reservations construction grammarians may have. Additionally, we delve into experiments that explore the emergence of constructionally relevant information within these models while also examining the aspects of Construction Grammar that may pose challenges for these models. This chapter aims to foster collaboration between researchers in the fields of natural language processing and Construction Grammar. By doing so, we hope to pave the way for new insights and advancements in both these fields