Showing papers by "Nello Cristianini published in 2014"
01 Oct 2014
TL;DR: The media discourse has shifted from one of public debate about nuclear power as a viable option for energy supply needs to a re-emergence of the public views of nuclear power and the risks associated with it, and the methodology used presents an opportunity to leverage big data for corpus analysis and opens up new possibilities in social scientific research.
Abstract: The contents of English-language online-news over 5 years have been analyzed to explore the impact of the Fukushima disaster on the media coverage of nuclear power. This big data study, based on millions of news articles, involves the extraction of narrative networks, association networks, and sentiment time series. The key finding is that media attitude towards nuclear power has significantly changed in the wake of the Fukushima disaster, in terms of sentiment and in terms of framing, showing a long lasting effect that does not appear to recover before the end of the period covered by this study. In particular, we find that the media discourse has shifted from one of public debate about nuclear power as a viable option for energy supply needs to a re-emergence of the public views of nuclear power and the risks associated with it. The methodology used presents an opportunity to leverage big data for corpus analysis and opens up new possibilities in social scientific research.
31 citations
TL;DR: The results show that the online embedding indeed approximates the geometry of the full corpus-wise TF and TF-IDF space and the model obtains competitive F measures with respect to the most accurate methods, using significantly fewer computational resources.
Abstract: We present a method for the classification of multi-labeled text documents explicitly designed for data stream applications that require to process a virtually infinite sequence of data using constant memory and constant processing time. Our method is composed of an online procedure used to efficiently map text into a low-dimensional feature space and a partition of this space into a set of regions for which the system extracts and keeps statistics used to predict multi-label text annotations. Documents are fed into the system as a sequence of words, mapped to a region of the partition, and annotated using the statistics computed from the labeled instances colliding in the same region. This approach is referred to as clashing. We illustrate the method in real-world text data, comparing the results with those obtained using other text classifiers. In addition, we provide an analysis about the effect of the representation space dimensionality on the predictive performance of the system. Our results show that the online embedding indeed approximates the geometry of the full corpus-wise TF and TF-IDF space. The model obtains competitive F measures with respect to the most accurate methods, using significantly fewer computational resources. In addition, the method achieves a higher macro-averaged F measure than methods with similar running time. Furthermore, the system is able to learn faster than the other methods from partially labeled streams.
22 citations
TL;DR: The present article examines that paradigm shift by using the conceptual tools developed by Thomas Kuhn, and by analysing the contents of the longest running conference series in the field by discussing the most recent such transition.
Abstract: The field of Artificial Intelligence AI has undergone many transformations, most recently the emergence of data-driven approaches centred on machine learning technology. The present article examines that paradigm shift by using the conceptual tools developed by Thomas Kuhn, and by analysing the contents of the longest running conference series in the field. A paradigm shift occurs when a new set of assumptions and values replaces the previous one within a given scientific community. These are often conveyed implicitly, by the choice of success stories that exemplify and define what a given field of research is about, demonstrating what kind of questions and answers are appropriate. The replacement of these exemplar stories corresponds to a shift in goals, methods, and expectations. We discuss the most recent such transition in the field of Artificial Intelligence, as well as commenting on some earlier ones.
18 citations
Posted Content•
TL;DR: A modular system by combining multiple AI modules into a flexible framework in which they can cooperate in complex tasks and allows the design and implementation of modular agents, where simple modules cooperate in the annotation of a large dataset without central coordination.
Abstract: Intelligent systems for the annotation of media content are increasingly being used for the automation of parts of social science research. In this domain the problem of integrating various Artificial Intelligence (AI) algorithms into a single intelligent system arises spontaneously. As part of our ongoing effort in automating media content analysis for the social sciences, we have built a modular system by combining multiple AI modules into a flexible framework in which they can cooperate in complex tasks. Our system combines data gathering, machine translation, topic classification, extraction and annotation of entities and social networks, as well as many other tasks that have been perfected over the past years of AI research. Over the last few years, it has allowed us to realise a series of scientific studies over a vast range of applications including comparative studies between news outlets and media content in different countries, modelling of user preferences, and monitoring public mood. The framework is flexible and allows the design and implementation of modular agents, where simple modules cooperate in the annotation of a large dataset without central coordination.
8 citations