Multimedia search without visual analysis: the value of linguistic and contextual information

This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features

The scholarly impact of TRECVid (2003-2009)

This paper reports on an investigation into the scholarly impact of the TRECVid (TREC Video Retrieval Evaluation) benchmarking conferences between 2003 and 2009. The contribution of TRECVid to research in video retrieval is assessed by analyzing publication content to show the development of techniques and approaches over time and by analyzing publication impact through publication numbers and citation analysis. Popular conference and journal venues for TRECVid publications are identified in terms of number of citations received. For a selection of participants at different career stages, the relative importance of TRECVid publications in terms of citations vis a vis their other publications is investigated. TRECVid, as an evaluation conference, provides data on which research teams ‘scored’ highly against the evaluation criteria and the relationship between ‘top scoring’ teams at TRECVid and the ‘top scoring’ papers in terms of citations is analysed. A strong relationship was found between ‘success’ at TRECVid and ‘success’ at citations both for high scoring and low scoring teams. The implications of the study in terms of the value of TRECVid as a research activity, and the value of bibliometric analysis as a research evaluation tool, are discussed

Fact, Fiction, and Fantasy

This paper argues: (1) All knowledge from fiction is from imagination (2) All knowledge from imagination is modal knowledge (3) So, all knowledge from fiction is modal knowledge Moreover, some knowledge is from fiction, so (1)-(3) are non-vacuously true

COWpads: Sharing iPads in a range of secondary school classrooms

This article outlines a mid-point snapshot of the progress of a small teaching-as-inquiry project at Hillcrest High School in 2013. Three teachers (music, mathematics, French) volunteered to focus on using iPads in a COW (computers on wheels, hence the term COWPads) configuration with a junior class during 2013. Each teacher created their own teaching-as-inquiry question focused on specific aspects of their practice. A University of Waikato researcher supported the teachers by observing classes and meeting regularly for feedback, reflection and discussion. Halfway through the year the following themes have emerged: the technical challenges to using a device designed for personal use as a shared device; a positive impact on students’ concentration levels and spans when using iPads, and shifts in teachers’ pedagogical design and practice. The teachers individually contribute their voices to this article, describing their initial experiences of using iPads on a regular basis and what they concentrated on most during the first few months of the project

The Senso Question Answering System at QA@CLEF 2008

This article has the Working Notes about the Universidade de Évora's participation in QA@CLEF2008 (http://www.clef-campaign.org/), based on the Senso question answer system and the Portuguese monolingual task

An overview of the linguistic resources used in cross-language question answering systems in CLEF Conference

The development of the Semantic Web requires great economic and human effort. Consequently, it is very useful to create mechanisms and tools that facilitate its expansion. From the standpoint of information retrieval (hereafter IR), access to the contents of the Semantic Web can be favored by the use of natural language, as it is much simpler and faster for the user to engage in his habitual form of expression. The growing popularity of Internet and the wide availability of web informative resources for general audiences are a fairly recent phenomenon, although man´s need to hurdle the language barrier and communicate with others is as old as the history of mankind. The World Wide Web, also known as WWW, together with the growing globalization of companies and organizations, and the increase of the non-English speaking audience, entails the demand for tools allowing users to secure information from a wide range of resources. Yet the underlying linguistic restrictions are often overlooked by researchers and designers. Against this background, a key characteristic to be evaluated in terms of the efficiency of IR systems is its capacity to allow users find a corpus of documents in different languages, and to facilitate the relevant information despite limited linguistic competence regarding the target language

Fully Automated Fact Checking Using External Sources

Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.Comment: RANLP-201