Annotating the meaning of discourse connectives in multilingual corpora

Discourse connectives are lexical items indicating coherence relations between discourse segments. Even though many languages possess a whole range of connectives, important divergences exist cross-linguistically in the number of connectives that are used to express a given relation. For this reason, connectives are not easily paired with a univocal translation equivalent across languages. This paper is a first attempt to design a reliable method to annotate the meaning of discourse connectives cross-linguistically using corpus data. We present the methodological choices made to reach this aim and report three annotation experiments using the framework of the Penn Discourse Tree Bank.

Discourse-level Annotation over Europarl for Machine Translation: Connectives and Pronouns

This paper describes methods and results for the annotation of two discourse-level phenomena, connectives and pronouns, over a multilingual parallel corpus. Excerpts from Europarl in English and French have been annotated with disambiguation information for connectives and pronouns, for about 3600 tokens. This data is then used in several ways: for cross-linguistic studies, for training automatic disambiguation software, and ultimately for training and testing discourse-aware statistical machine translation systems. The paper presents the annotation procedures and their results in detail, and overviews the first systems trained on the annotated resources and their use for machine translation.

Towards Automatic Identification of Discourse Markers in Dialogs: the Case of 'Like'

This article discusses the detection of discourse markers (DM) in dialog transcriptions, by human annotators and by automated means. After a theoretical discussion of the definition of DMs and their relevance to natural language processing, we focus on the role of like as a DM. Results from experiments with human annotators show that detection of DMs is a difficult but reliable task, which requires prosodic information from soundtracks. Then, several types of features are defined for automatic disambiguation of like: collocations, part-of-speech tags and duration-based features. Decision-tree learning shows that for like, nearly 70% precision can be reached, with near 100% recall, mainly using collocation filters. Similar results hold for well, with about 91% precision at 100% recall.

Regionalist Protest through Shared Rule? Peripherality and the Use of Cantonal Initiatives in Switzerland

In this article, we are interested in the extent to which federalism is able to deal with peripheral protest through shared rule channels. Shared rule as a key dimension of federal states has not thus far received adequate academic attention. Empirically, we analyse the use of all cantonal initiatives in Switzerland over the past 25 years as a particular instrument of shared rule, subsequently focusing on two peripheral regions with successful regionalist parties, Ticino and Geneva. We find that regionalist parties contribute towards radicalizing peripheral demands in search of attention from the centre. This leads to the mainstreaming of peripheral demands by pulling other parties along. We conclude that shared rule properly designed gives even the most peripheral regions a voice in national decisions, but that regionalist parties may also use shared rule instruments to mobilize their electorate at home to fight their non-regionalist competitors.