The Online Faculty Development and Assessment System

This article evaluates the role of the Online Faculty Development and Assessment System (OFDAS), created at universities in the Canary Islands, Spain, in staff development. The evaluation indicates that the system helped staff in learning to teach curriculum and teaching capacities. The tasks, online resources and opportunities for discussions provided within the learning environment created for the system helped shape their attitudes towards learning curriculum and teaching capacities and enabled them to share their concerns about students’ classroom learning environment assessment

The CHORUS gap analysis on user-centered methodology for design and evaluation of multi-media information access systems

CHORUS is a Coordination Action, a specific type of project funded by the European commission under its research programmes, intended to bring together research projects with common goals, in the field of search technologies for digital audio-visual content, one of the strategic objectives of the current research frame program. CHORUS coordinates a number of research projects in the general area of audio-visual and multi-media information access and management. The most important single contribution of the CHORUS work plan will be to provide a survey of the field and a roadmap with a gap analysis for the realisation of viable audio-visual search engines by European partners. This is done by several means. CHORUS organises Think-Tanks with industrial participation, focussed workshops to treat specific questions, and more general conferences for academic discussions. CHORUS is now in its final phase, and is currently preparing its final report together with a final conference to mark its publication

Towards a Knowledge Graph based Speech Interface

Applications which use human speech as an input require a speech interface with high recognition accuracy. The words or phrases in the recognised text are annotated with a machine-understandable meaning and linked to knowledge graphs for further processing by the target application. These semantic annotations of recognised words can be represented as a subject-predicate-object triples which collectively form a graph often referred to as a knowledge graph. This type of knowledge representation facilitates to use speech interfaces with any spoken input application, since the information is represented in logical, semantic form, retrieving and storing can be followed using any web standard query languages. In this work, we develop a methodology for linking speech input to knowledge graphs and study the impact of recognition errors in the overall process. We show that for a corpus with lower WER, the annotation and linking of entities to the DBpedia knowledge graph is considerable. DBpedia Spotlight, a tool to interlink text documents with the linked open data is used to link the speech recognition output to the DBpedia knowledge graph. Such a knowledge-based speech recognition interface is useful for applications such as question answering or spoken dialog systems.Comment: Under Review in International Workshop on Grounding Language Understanding, Satellite of Interspeech 201

Inférences réflexives dans la publicité

Advertisements are so ubiquitous nowadays that capturing the addressee’s attention and maintaining it long enough for them to be fully processed have become fundamental objectives for advertisers. Employing specific strategies in the design of the advertisement contributes efficiently to achieving these goals, getting the audience not only to attend the stimulus but also to process it in certain ways favourable for the advertiser. We argue that Relevance theory, an approach to communication built on a massively modular view of cognition, offers the right tools to explain the nature of the interpretative processes in verbal comprehension. Knowledge of the relevance-based reflexive inferential procedures involved in utterance interpretation allows advertisers to foresee the addressee’s processing behaviour, giving them the possibility to control it in a such a way that the intended interpretative effects are achieved in the desired way

WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking

We present WISER, a new semantic search engine for expert finding in academia. Our system is unsupervised and it jointly combines classical language modeling techniques, based on text evidences, with the Wikipedia Knowledge Graph, via entity linking. WISER indexes each academic author through a novel profiling technique which models her expertise with a small, labeled and weighted graph drawn from Wikipedia. Nodes in this graph are the Wikipedia entities mentioned in the author's publications, whereas the weighted edges express the semantic relatedness among these entities computed via textual and graph-based relatedness functions. Every node is also labeled with a relevance score which models the pertinence of the corresponding entity to author's expertise, and is computed by means of a proper random-walk calculation over that graph; and with a latent vector representation which is learned via entity and other kinds of structural embeddings derived from Wikipedia. At query time, experts are retrieved by combining classic document-centric approaches, which exploit the occurrences of query terms in the author's documents, with a novel set of profile-centric scoring strategies, which compute the semantic relatedness between the author's expertise and the query topic via the above graph-based profiles. The effectiveness of our system is established over a large-scale experimental test on a standard dataset for this task. We show that WISER achieves better performance than all the other competitors, thus proving the effectiveness of modelling author's profile via our "semantic" graph of entities. Finally, we comment on the use of WISER for indexing and profiling the whole research community within the University of Pisa, and its application to technology transfer in our University