Weak and strong discourse markers in speech, chat and writing:Do signals compensate for ambiguity in explicit relations?

Ambiguity in discourse is pervasive, yet mechanisms of production and processing suggest that it tends to be compensated in context. The present study sets out to analyze the combination of discourse markers (such as but or moreover) with other discourse signals (such as semantic relations or punctuation marks) across three genres (discussion, chat, and essay). The presence of discourse signals is expected to vary with the ambiguity of the discourse marker and with the genre. This analysis complements recent approaches to discourse signalling by zooming in on the different types of discourse markers with which other signals combine. The corpus annotation study uncovered three categories of marker strength—weak, intermediate, and strong—thus refining the concept of “explicitness.” Statistical modeling reveals that weak discourse markers are more often compensated than intermediate and strong markers, and that this compensation is not affected by genre variation

Exploring the Fuzzy Boundaries of Discourse Markers Through Manual and Automatic Annotation

Discourse markers are non-propositional linguistic items that are notoriously difficult to identify as well as to categorize. We can observe several borderline phenomena and overlaps with other formal and functional categories, for example inserts, adverbials, contextualization cues, pragmatic force modifiers and so on. By way of addressing such overlaps as well as the disambiguation between discourse marker uses and their source categories, the chapter presents a comparison of automated and manual annotation of oral discourse markers (discourse markers). Firstly, an overview of the criterial features of discourse markers that are relevant to disambiguation is presented. Secondly, the UCREL Semantic Analysis System (USAS) and its disambiguation methods are briefly discussed. In the third part of the chapter manual and automatic decisions about categorization are compared with a view to addressing the margin of error reported to apply in general semantic annotation as well as the question of what formal-functional properties of the relevant discourse markers might explain possible differences between manual and automatic annotation