Francesca Bonin

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In this paper we present a novel approach to multi–word terminology extraction combining a well–known automatic term recognition approach, the C–NC value method, with a contrastive ranking technique, aimed at refining obtained results either by filtering noise due to common words or by discerning between semantically different types of terms within(More)
Modelling, analysis and synthesis of behaviour are the subject of major efforts in computing science, especially when it comes to technologies that make sense of human–human and human–machine interactions. This article outlines some of the most important issues that still need to be addressed to ensure substantial progress in the field, namely (1)(More)
Conversation is widely studied through corpus analysis, often concentrating on ‘task-based’ interactions such as information gap activities (map-tasks [1], spot the difference [2], ranking items [3]) and real or staged business meetings [4], [5]. This task-based dialogue (on which spoken dialogue technology is based [6]) relies heavily on verbal information(More)
One of the main challenges of recent years is to create socially intelligent machines: machines able not only to communicate but also to understand social signals and make sense of the various social contexts. In this paper we describe a new annotation method for the analysis of involvement, aiming at exploring the relation between the individual and the(More)
This study explores laughter distribution around topic changes in multiparty conversations. The distribution of shared and solo laughter around topic changes was examined in corpora containing two types of spoken interaction; meetings and informal conversation. Shared laughter was significantly more frequent in the 15 seconds leading up to topic change in(More)
This paper reports a study of attitude manifestations in video blogs. We describe the manual annotation of speaker attitudes in a corpus of over 130 video blogs and present an analysis of prosodic and visual cues in relation to attitude states. We use machine learning techniques for the automatic prediction of attitudes from prosodic and visual features in(More)