Adapting Progress Feedback and Emotional Support to Learner Personality

Peer reviewedPostprin

Production of Referring Expressions for an Unknown Audience : a Computational Model of Communal Common Ground

The research reported in this article is based on the Ph.D. project of Dr. RK, which was funded by the Scottish Informatics and Computer Science Alliance (SICSA). KvD acknowledges support from the EPSRC under the RefNet grant (EP/J019615/1).Peer reviewedPublisher PD

Designing emotional support messages tailored to stressors

Acknowledgements This work was funded by the RCUK Digital Economy award to the dot.rural Digital Economy Hub, University of Aberdeen; award reference: EP/G066051/1. The dataset used by this paper can be acquired by emailing the first author. We thank Matt Dennis, Kirsten A. Smith and Michael Gibson for their contributions to the research.Peer reviewedPublisher PD

Evaluating Centering for Information Ordering Using Corpora

In this article we discuss several metrics of coherence defined using centering theory and investigate the usefulness of such metrics for information ordering in automatic text generation. We estimate empirically which is the most promising metric and how useful this metric is using a general methodology applied on several corpora. Our main result is that the simplest metric (which relies exclusively on NOCB transitions) sets a robust baseline that cannot be outperformed by other metrics which make use of additional centering-based features. This baseline can be used for the development of both text-to-text and concept-to-text generation systems. </jats:p

Using Technology to Enhance Rural Resilience in Pre-hospital Emergencies

The research presented in this paper is supported by RCUK dot.rural Digital Economy Research Hub, University of Aberdeen [grant number EP/G066051/1].Peer reviewedPublisher PD

Defeasible Rules in Content Selection and Text Structuring

This paper outlines a number of ways in which defeasible rules can contribute to the content selection and discourse structuring components of a text generation system. We suggest that, for certain types of descriptive text, the characterisation of discourse structuring mechanisms as operations on or involving defeasible rules provides an attractive framework for addressing important issues in content selection/structuring. We describe an architecture which incorporates defeasible rules into a systemic model of generation, and illustrate its use in the description of objects in a museum gallery. While defeasible rules are traditionally used in theorem-proving applications, to make predictions about the consequences of known facts, we are here concerned with three separate issues: (i) how such rules may need to be expressed by the NLG system in order to achieve its goals; (ii) how their interaction with facts about particular objects enables the use of valuable coherence relations; and ..

Creating sustainable digital community heritage resources using linked data.

The CURIOS project investigates how digital archives can support interest in local heritage and, in doing so, can contribute to community regeneration and strengthened community cohesion. Software tools that utilise semantic web/ linked data technology are being developed to build a general, flexible and 'future proof' software platform to assist remote rural communities to collaboratively maintain and present information about their cultural heritage. Under this broad programme of research we are investigating how online cultural communities are transforming the ways in which local history is 'written' and remembered. Empirically, we focus on digital cultural heritage resources managed by community groups in remote and rural parts of the UK. Researching community-led initiatives enables us to explore how locally managed digital heritage resources can support sustainable rural areas

The 'audioview' - providing a glance at Java source code

Presented at the 11th International Conference on Auditory Display (ICAD2005

Crowdsourcing Without a Crowd: Reliable Online Species Identification Using Bayesian Models to Minimize Crowd Size

We present an incremental Bayesian model that resolves key issues of crowd size and data quality for consensus labeling. We evaluate our method using data collected from a real-world citizen science program, BeeWatch, which invites members of the public in the United Kingdom to classify (label) photographs of bumblebees as one of 22 possible species. The biological recording domain poses two key and hitherto unaddressed challenges for consensus models of crowdsourcing: (1) the large number of potential species makes classification difficult, and (2) this is compounded by limited crowd availability, stemming from both the inherent difficulty of the task and the lack of relevant skills among the general public. We demonstrate that consensus labels can be reliably found in such circumstances with very small crowd sizes of around three to five users (i.e., through group sourcing). Our incremental Bayesian model, which minimizes crowd size by re-evaluating the quality of the consensus label following each species identification solicited from the crowd, is competitive with a Bayesian approach that uses a larger but fixed crowd size and outperforms majority voting. These results have important ecological applicability: biological recording programs such as BeeWatch can sustain themselves when resources such as taxonomic experts to confirm identifications by photo submitters are scarce (as is typically the case), and feedback can be provided to submitters in a timely fashion. More generally, our model provides benefits to any crowdsourced consensus labeling task where there is a cost (financial or otherwise) associated with soliciting a label