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There have been several attempts to annotate communicative functions to utterances of verbal feedback in English previously. Here, we suggest an annotation scheme for verbal and non-verbal feedback utterances in French including the categories base, attitude, previous and visual. The data comprises conversations, maptasks and negotiations from which we extracted ca. 13,000 candidate feedback utterances and gestures. 12 students were recruited for the annotation campaign of ca. 9,500 instances. Each instance was annotated by between 2 and 7 raters. The evaluation of the annotation agreement resulted in an average best-pair kappa of 0.6. While the base category with the values acknowledgement, evaluation, answer, elicit and other achieves good agreement, this is not the case for the other main categories. The data sets, which also include automatic extractions of lexical, positional and acoustic features, are freely available and will further be used for machine learning classification experiments to analyse the form-function relationship of feedback.
Annotating Discourse Relations in Spoken Language: A Comparison of the PDTB and CCR Frameworks
(2016)
In discourse relation annotation, there is currently a variety of different frameworks being used, and most of them have been developed and employed mostly on written data. This raises a number of questions regarding interoperability of discourse relation annotation schemes, as well as regarding differences in discourse annotation for written vs. spoken domains. In this paper, we describe ouron annotating two spoken domains from the SPICE Ireland corpus (telephone conversations and broadcast interviews) according todifferent discourse annotation schemes, PDTB 3.0 and CCR. We show that annotations in the two schemes can largely be mappedone another, and discuss differences in operationalisations of discourse relation schemes which present a challenge to automatic mapping. We also observe systematic differences in the prevalence of implicit discourse relations in spoken data compared to written texts,find that there are also differences in the types of causal relations between the domains. Finally, we find that PDTB 3.0 addresses many shortcomings of PDTB 2.0 wrt. the annotation of spoken discourse, and suggest further extensions. The new corpus has roughly theof the CoNLL 2015 Shared Task test set, and we hence hope that it will be a valuable resource for the evaluation of automatic discourse relation labellers.
Bericht über die 19. Arbeitstagung zur Gesprächsforschung vom 16. bis 18. März 2016 in Mannheim
(2016)
Comparaison de deux marqueurs d’affirmation dans des séquences de co-construction: voilà et genau
(2016)
This contribution investigates the German response particle genau and the French response particle voilà within collaborative turn sequences in videotaped ordinary conversations. Adopting a conversation analytic approach to cross-linguistic comparison, I will show that the basic epistemic value of both particles allows them to be used in similar sequential environments. When a co-participant formulates a candidate conclusion in environments where it can be easily inferred from previous talk, first speakers may confirm the adequacy of the pre-emptive completion by voilà or genau. These particles may then also be followed by self- or other-repeats. The analyses aim to illustrate that participants rely on a variety of practices in order to positively assess a pre-emptive completion, and to refute a supposed binary opposition of refusal vs. acceptance in the receipt slot.
This paper is about the workflow for construction and dissemination of FOLK (Forschungs - und Lehrkorpus Gesprochenes Deutsch – Research and Teaching Corpus of Spoken German), a large corpus of authentic spoken interaction data, recorded on audio and video. Section 2 describes in detail the tools used in the individual steps of transcription, anonymization, orthographic normalization, lemmatization and POS tagging of the data, as well as some utilities used for corpus management. Section 3 deals with the DGD (Datenbank für Gesprochenes Deutsch - Database of Spoken German) as a tool for distributing completed data sets and making them available for qualitative and quantitative analysis. In section 4, some plans for further development are sketched.
Diese Handreichung stellt die Datenbank für Gesprochenes Deutsch (DGD) und speziell das Forschungs- und Lehrkorpus Gesprochenes Deutsch (FOLK) als Instrumente gesprächsanalytischer Arbeit vor. Nach einem kurzen einführenden Überblick werden anhand des Beispiels "sprich" als Diskursmarker bzw. Reformulierungsindikator Schritt für Schritt die Ressourcen und Tools für systematische korpus- und datenbankgesteuerte Recherchen und Analysen vorgestellt und illustriert.